Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
Authors: Yifan Wang
Mayank Jobanputra
Ji-Ung Lee
Soyoung Oh
Isabel Valera
Vera Demberg
Venue: ICLR 2026 Poster (2026)
Keywords: Fairness
Explainability
Hate speech detection
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We study whether, and in what ways, input-based explanations can be used to detect biased predictions, select fair models, and mitigate biases during model training.

Abstract: Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness.
At the same time, their black-box nature makes it difficult for users to recognize biased predictions and for developers to effectively mitigate them.
While some studies suggest that input-based explanations can help detect and mitigate bias, others question their reliability in ensuring fairness.
Existing research on explainability in fair NLP has been predominantly qualitative, with limited large-scale quantitative analysis.
In this work, we conduct the first systematic study of the relationship between explainability and fairness in hate speech detection, focusing on both encoder- and decoder-only models.
We examine three key dimensions: (1) identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training.
Our findings show that input-based explanations can effectively detect biased predictions and serve as useful supervision for reducing bias during training, but they are unreliable for selecting fair models among candidates.
Our code is available at https://github.com/Ewanwong/fairness_x_explainability.

Published as a conference paper at ICLR 2026
BRIDGING FAIRNESS AND EXPLAINABILITY: CAN INPUT-BASED EXPLANATIONS PROMOTE FAIRNESS IN HATE SPEECH DETECTION?
Yifan Wang', Mayank Jobanputra', Ji-Ung Lee', Soyoung Oh',
Isabel Valera Vera Demberg!2
Saarland University, Germany
2 Max Planck Institute for Informatics, Saarland Informatics Campus, Germany
3 Max Planck Institute for Software Systems, Saarland Informatics Campus, Germany
(y Fwang, mayank, soyoung, vera (erraelsst @lst. uni saar 1 and de,
1 -ung- leeθuni -saa maaalaan landd de, i iv 1 era@cs. uni seri-ssarlee waarland.dde
ABSTRACT
Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about faimess. At the same time, their black- box nature makes it difficult for users to recognize biased predictions and for de- velopers to effectively mitigate them. While some studies suggest that input- based explanations help detect and mitigate bias, others question their reliability in ensuring fairness. Existing research on explainability in fair NLP has been pre- dominantly qualitative, with limited large-scale quantitative analysis. In this work, we conduct the first systematic study the relationship between explainability and fairness in hate speech detection, focusing both encoder- and decoder-only models. We examine three key dimensions: (1) identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training. Our find- ings show that input-based explanations can effectively detect biased predictions and serve as useful supervision for redueing bias during training, but they are unreliable for selecting fair models among candidates. Our code is available at https://ggiub..co///wwwwnnng wexp:ammmesss.eeppxiiiaiilllg
INTRODUCTION
Language models (LMs) pre-trained on large-scale natural language datasets have shown great pacities in various NLP tasks (Wang et al. 2018 Gao et al. 2023). However, previous studies have shown that they can replicate and amplify stereotypes and social bias present in their training data and demonstrate biased behaviors (Sheng et al. 2021 Gupta et al. 20244] Gallegos et al. (024). Such behaviors risk the underrepresentation of marginalized groups and the unfair allocation of resources, raising serious concerns in critical applications (Blodgett et al., 2020).
Meanwhile, current NLP models are mostly based black-box neural networks. Despite their strong capacities, the complex architecture and large number of parameters of these models make it hard for humans understand their behaviors (Bommasani al. 202T). To understand neural NLP models, different types of explanations have been devised, such as input-based based explanations (Yin & Neubig 2022 Deiseroth al. 2023 Madsen et al., 2024 Wang al. (0256), natural language explanations (Ramnath et al. 2024 Wang et al. 2025a). and concept-based explanations (Yu et al.) 2024 Raman et al., 2024). Among these, input-based explanations, often referred to as rationales, indicate the contribution of each token models' predictions, and thus provide the most direct insights into models' behaviors (Arras et al. 2019 Atanasova et al. 2022 Lyu et al. 2024).
Explainability has long been deemed critical to improving faimesss. Researchers believe that if the use of sensitive features is evidenced by model explanations, then they can easily detect biased predictions and impose fairness constraints by guiding models to avoid such faulty reasoning (Meng et al.] 20222; Sogancioglu et al.] 2023). However, recent studies have challenged this assumption, suggesting that the relationship between explainability and faimess is complex and that explanations

---

Published as a conference paper at ICLR 2026
may not always reliabby detect mitigate bias (Dimanov et al. 2020: Slack et al. 2020 Pruthi et al. 2020). Unfortunately, to the best of our knowledge, current studies are mostly limited to qualitative analysis on a small set of explanation methods (Balkir al. 2022 Deck et al. 2024). Our work takes a step toward bridging explainability and fairness by providing the first comprehensive quantitative analysis in the context of hate speech detection, task where both faimess and explainability
particularly critical. Specifically, we address the following three research questions to investigate the role of explainability in promoting fairness within the task of hate speech detection:
RQQ:: Can input- explanations be used to identify biased predictions?
RQ2: Can input-based explanations be used to automatically select fair models?
RQ3: Can input-based explanations be used to mitigate bias during model training?
Our experiments demonstrate that input-based explanations can effectively detect biased predictions (RQ1), are less reliable for automatic fair model selection (RQ2), and can help reduce bias during model training (RQ3). Furthermore, our analyses indicate that explanation-based bias detection remains robust even when models are trained to reduce reliance on sensitive features, and that these explanations outperform LLM judgments in identifying bias.
2 RELATED WORK
Bias in NLP The presence of social bias and stereotypes has significantly shaped human language and LMs trained on (Blodgett al.] 2020, (hhnng al., 202T). As a result, these models often exhibit biased behaviors (Gallegos et al. 2024), such as stereotypical geographical relations in the embedding space (Bolukbasi et al., 2016 May et al. 2019) and stereotypical associations between social groups and certain concepts in the model outputs (Fang et al. 2024 Wan & Chang. 2025) More critically, disparities in model predictions and performance across social groups (Zhao et al., 2018 (Sheng et al., (219) can significantly compromise user experiences of marginalized groups and risk amplifying bias against them, therefore drawing great concerns in critical use cases.
Input-based Model Explanations Input-based explanations in NLP models aim to attribute model predictions to each input token (Lyu et al., 2024). They can be broadly categorized based on how they generate explanations: gradient-based (Simonyan et al. 2014, Kindermans et al. 2016 Sundararajan et al.) 2017) Enguchard (2023), propagation-based (Bach al.] 2015 Shrrkkuar al.
Ferrando et al.
2022; Modarressi et al. 2022, 2023). perturbation based (Li et al.) 2016
Ribeiro al.; [0166] Lundberg & Leel [017]] Deiseroth et al. 2023), and attention-based meth- 2015 Abnar & Zuidema, (020). While most prior work has focused on
ods (Bahdanau et al.,
encoder-only models, recent studies have also explored explaining the behaviors of generative mod-
els (Yin & Neubig 2022 Ferrando et al., 2022: Enouen et al. 2024 Cohen- Wang et al. 2024).
Bridging Explainability and Fairness Explainability is often considered essential for achieving fairness in machine learning systems (Balkir et al. [2022] Deck et al.) 2024). One line of research investigates model bias by analyzing explanations (Prabhakaran et al. [2019]]eyara] & Delany 2024 Sogancioglu al.) 2023). For instance, Muntasir & Noor (2025) shows that biased model relied on gendered words as key features in its predictions, as revealed by LIME explanations. Similarly, Stevens al. (2020) demonstrates that biased models often place high importance on gender and race features when examined with SHAP explanations. Extending this line of evidence, |Mng et al. (2022) finds that features with higher importance scores are associated with larger disparities in model performance on a synthetic medical dataset using deep learning models.
Another line of research focuses on mitigating bias with explanations (Dimanov et al.) 2020 Kennedy et al. 2020 Rao al.) 2023 Liu et al.) 2024). For example, Hickey et al. (2020) im- proves fairness by reducing reliance on sensitive features during training with SHAP explanations. Bhargava et al. (2020) and (Gonálezz-ille Silot al. (2025) first identify predictive sensitive features
using LIME and SHAP, respectively, and then remove them prior to model training. Relatedly, Grabowicz et al. (2022) traces unfaimesss metrics to input features and adjusts them to mitigate bias.
However, recent research has challenged the assumption that input-based explanations can be reli- ably used detect and mitigate bias. First, current explanation methods may be unfaithful, meaning
2

---

Published as a conference paper at ICLR 2026
RQ1: Bies Detection
Searthis Iolens
Predicioo
RQ2: Fair Model Selection
0.23
compleuted wikteeddyy
0.01
0:77
Regresact
0.25
0:01
What
that steet mesounters
0.71
0.00
Coneriators 0.56
Bank Comnintarn 0.45.
RQ3: Blas Mitigation
Vallderten
Mervi Mork
Ralianie
01.25
5.15
OUBT
Figure I:
Workflow diagram illustrating the processes used to address each research question. Sen- sitive tokens are shown in blue boxes, and the intensity of the green shading reflects each word's s contribution to the model's prediction.
that they may not always reflect the true decision-making process of models (Jain & Wallace 2019
Ye et al. 2025). This makes difficult reliably detect the use of sensitive features in predictions. Second, efforts to reduce the influence of sensitive features can lead to unintended consequences, sometimes degrading both task performance and fairness of models (Dimanov et al. 2020). Finally, models can be deliberately trained to assign lower importance to sensitive features, thereby masking biased predictions when explanations are inspected (Slack et al. 2020 Pruthi et al. 2020).
Despite growing interest in this topic, most existing work remains qualitative or restricted to limited setups (Balkir et al. 2022, Deck et al., 2024). To the best of our knowledge, this the first study to quantitatively and comprehensively examine the relationship between explainability and fairness in NLP models. We focus on hate speech detection as a particularly critical application. Prior research has shown that biased NLP models often rely on demographic information such as race and gender, leading to inferior performance on marginalized groups in this task (Sap et al., 2019 Mathew et al.) therefore essential for equitable social media participation across all groups. We define hate speech and social bias and review related fairness and explainability work in hate speech detection in Appendix A
2021]. Detecting and mitigating such biased behaviors
EXPERIMENTAL SETUP
Notations Let an input text x consist of tokens t,, f2.....,t, The task of hate speech detection is to predict binary label ÿ E [toxic, non-toxic]. A classifier outputs the probability of class c as fc(x), where f is implemented by a neural model.
In the context of social bias, we assume that bias type (e.g., race)) involves a set of social groups G (e.g., black, white,..) ...). subset of tokens ' loga..., 'gm in x denotes the sensitive feature g E G of the speaker target. We refer to these tokens as sensitive lokens. By replacing the sensitive tokens of group g with those of another group g', we obtain a counterfactual version of x that refers to g', denoted as (0))
An input-based explanation assigns attribution score to each token in x for class 0j ....... a indicating their contribution to the prediction of class c. Following Dimanov et al. (2020), we com- " which we refer to the sensitive token reliance scores. To handle cases where multiple sensitive tokens appear in the same sentence, take the maximum absolute attribution value the reliance score for that example
pute attribution scores the sensitive tokens, aG ag ......., ..
sensitive token reliance(x, c) af.-, where j" arg max JE ((1..........
|ag]
We have experimented with normalizing feature importance scores but found that using raw scores yielded the best results. We also evaluated sum and average aggregation methods beyond taking the max absolute value and observed similar outcomes.
3

---

Published as a conference paper at ICLR 2026
Datasets and Vocabulary We two hate speech detection datasets: Civil Comments (Borkan et al. 2019) and Jigsaw (cjadams et al. 2019). To ensure coverage, we focus on three bias types and their associated groups: race (black/white), gender (femalc/male)), and religion (Chris- tian/Muslim/Jewishh.. We include examples containing identity-marking terms but exclude those with derogatory slur-based references, the latter can reasonably serve direct evidence for toxic predictions. The sensitive token vocabulary is derived from Caliskan et al. (2017) and Wang
& Demberg 2024a). Further details on dataset pre-processing are provided in Appendix B
Models We evaluate major classes of NLP models: encoder-only models (BERT DDvllin it al. 2019) and RoBERTa (Liu et al. 2019) and decoder-only large language models (Llama3.2 2-2- 3B-Instruct (Dubey al. 2024). Qwen3-4B, and Qwen3-8B (Yang et al. 2025a), all of which are aligned to human values). We thnettun encoder-only models on data subsets that either target single bias type or combine all bias types. For decoder-only models, we use an instruction- setup where the model is prompted to decide whether a test example contains hate speech. The prompt includes the definition of hate speech, the test example, and a corresponding question. As baseline, adopt the zero-shot setting the default configuration.
Beyond conventional fine-tuning and prompting, we also investigate the interaction between ex- plainability and fairness in debiased models. For encoder-only models, we apply pre-processing techniques such as group balance (Kamiran & Calders) 2012), group-class balance (Dixon et al, 2018), and counterfactual data augmentation (CDA, Zmigrod et al. (019)). as well as in-processing techniques including dropout (Webster et al., 2020). attention entropy (Attanasio et al.) (022). and causal debias (Zhou al., (023). For decoder-only models, we incorporate bias reduction through prompt design, including few-shot, fairness imagination (Chen et al. 2025), and fairness instruction prompting (Chen et al. (2025). We do not include reasoning models and thain-of-thouge prompting, as we find that their predictions are primarily attributed to intermediate reasoning steps rather than the input text, which complicates analysis and falls beyond the scope of this work. Further details are provided in Appendix
Fairness Metries We evaluate fairness in model predictions using two categories of metrics: group fairness and individual fairness. Group fairness metrics capture disparities in performance across demographic groups:
Dispmetric
metric, metrical.
SEG
where metrica is the average metric value across all groups G in a bias type. We specifically measure disparities in accuracy (ACC), false positive rate (FPR), and false negative rate (FNR).
Individual fairness measures the extent to which model's prediction for given example changes when the associated social group is altered. To maintain consistency with the direction group fairness metries, compute the individual unfairness (IU) score of x; and the predicted class ÿ.:
IU(x,) = (x,)
Σ
Sg. (x(
Go
g'E))(or)
The Average IU score (Avg,) is then computed over dataset to reflect the overall level of individual unfairness model.
For both types of metrics, higher scores indicate more bias in model predictions. It is worth noting that individual unfairness can be evaluated at the level each example, whereas group fairness metrics are defined over sets of validation or test examples. To compute the fairness metrics, we randomly sample a subset of examples for each bias type such that each social group contributes an equal number of examples. Further details test set sampling are provided in Appendix
Explanation Methods We employ 16 variants of commonly used input-based post-hoc explana- tion methods, selected to represent a diverse range of methodological categories: Attention (Bah-
danau et al. 2015), Attention rollout (Attn rollout, Abbar & Zuidemal 2020), Attention flow (Attn
flow, Abnar & Zundemal 2020), Gradient (Grad, [Simonyan et al. 2014)), Input Gradient (IxG, Kin
dermans et al. 2016), Integrated Gradients (IntGrad, (Sundararajan al. 2017), Occlusion (Li al.
4

---

Published as a conference paper at ICLR 2026
2016). Liet (Shrikumar et al. 2017), KernelSHAP (undbbrg & Leel 2017 DecompX (Modar- ressi et al. 2.23). and Progressive Inference (ProgInfer, Kariyappa et al., 2024 For methods that attribute predictions to embeddings. we aggregate attribution scores into single feature importance value using either the mean or the 1.2 norm. For Occlusion, we additionally report results obtained by taking the absolute value of each attribution score prior computing sensitive token reliance scores (denoted as Occlusion abs). The time and GPU memory costs for each method are shown in Appendix [ We also study rationales generated by LLMs and find that they are not as reliable as input-based explanations in detecting bias (Section 6).
Table 1: Task performance and faimess of default and debiased models on Civil Comments. Results are provided for race/gender/reliiion biases. Green (red) indicates the results are better (worse) than the default/zero shot models. No debiasing method consistently reduces bias across all metrics and bias types.
Model
Method
Accuracy (7)
Disp... (4)
(1))
Disp
Avg
Default
18.38/88.05885.99 93
3.05/3.30/88.07
0.50/0.03/5.77
3,17/0,66/1227
Group balance
79,25/87 25/66.83
3.10//.800/3355 3.55
0.25/1.33/11.
10.46/5 38/30,31
3.79/0.2//200
Group-class balancing
7.288777//8777
1.8//275/14477
2.42/0.99/3.09
10.63/7-26/33.14
4.43/0.98/0.71
BERT
CDA
76.83/66.7000 70384863
1.35/3.60/144.1
5.88/2.00/5. 67
18.45/7.57/244
0,5000.5000. 90
Dropout
0..5388.20085.00
1.25/2.10V15.8
0.78/1 46/5,99
00.82/3.50/27.0
3.430.52/1.51
Attention entropy
79.15/87.670 67774,67/4499 93
2.60/2.05/15007
0.99/0. 10/4,99
11,71/7. 11/26.52
2,95/0.67/1 .58
Causal debias
18.80/66111/86.444
0.00/2.65/16.44
3.90/0.46/8882
7,98/10.67/30..
3.83/048//210
Zero-shot
09.55/79.75/77.5
0.60/.00011.444
7.13/1
13,25/3.71/5 15
2.55/2.41/33.
Qwen3-4B
Few-shot
70.15/80.73/79.2
1.800.65/18.99
10.02/2. 50/19.31
11, 118.999/91555555
3.18/3.34/3.76
Fairness imagination
11.23/80440880.3
0.85//.00/18.27
4.032.111110.51
11.62/9.211 21/992
2,98/3. 16/2.20
Fairness instruction
70.40/79.71/8047
0.60/1.35/19.33
4.300039/4.67
11.11/5.2415.0m .8
2.02/1 83/171
4 QUANTITATIVE ANALYSES OF FAIRNESS AND EXPLAINABILITY
To comprehensively understand the relationship between explainability and fairness in NLP mod- els, we examine three ways in which model explanations can be applied to promote fairness. The subsequent sections detail the experimental setups for each application and report the corresponding results. The workflow for our research questions shown in Figure For brevity, we report results on Civil Comments using BERT trained on single bias types and Qwen3-4B. We further demon- strate that our findings extend to other models and the Jigsaw dataset (Appendix D and that they generalize across tasks, model alignment types, and sensitive token sets beyond those considered in the main experiments (Appendix
4.1 MODEL PERFORMANCE AND FAIRNESS
As a prerequisite, we first summarize the performance and f'arrness of the evaluated models. The results in Table I show that no single debiasing method consistently improves all fairness metrics. For BERT and Qwen3-4B, CDA and fairness instruction achieve the largest reductions in individual unfairness, yet they may simultaneously amplify biases on other metrics. Other debiasing methods show a similar pattern: they reduce bias for specific metric or bias type, but the improvement does not generalize across different setups. These limitations underscore the importance of leveraging ex- planations for bias detection and mitigation. We find similar results for other models and for Jigsaw, which we provide in Appendix D along with discussion model performance and fairness.
4.2 RQI: EXPLANATIONS FOR BIAS DETECTION
Our first research question asks whether explanations can be used to detect biased predictions. We address the question through three steps: (1) obtain model predictions and compute individual un- fairness scores; (2) generate baset explanations for the predictions; and (3) compute sensitive token reliance scores and evaluate their Pearson correlation with individual unfairness, which we refer to as fairness correlation. A higher fairness correlation indicates that the explanation method
We apply DecompX only to encoder-only models and Progressive Inference only decoder-only models, following the setups of the original papers.
5

---

Published as a conference paper at ICLR 2026
is more effective in identifying predictions with high individual unfairesss. To ensure robustness, we compute the fairness correlation separately for each prediction class class-group group pair and report the average absolute score the final result for each explanation method.
We present results for default and debiased models where individual unfairness remains high after debiasing, as bias detection is particularly critical in these cases. Specifically, we report results for models with the highest average Avg,, scores across bias types, namely default, group balance, and causal debias for BERT, and zero-shot, few-shot, and fairness imagination prompting for Qwen3-4B. Results for religion as well as other models and the Jigsaw dataset are provided in Appendix
Defout
Group balance
Causal debias
Falmess conelatton
Attention
6.4
Attn rellout
Attn flow
lec mean
Gender
Gender
Gender
IntGrad L2
Owen3-4B
DespLift mean
imagination
Deepliift Occlusion
Falmess comrlation
4.4
Geclusion abs
KemeISHAP
8.4
Decompx
Proginfer
Gender
Ciencier
Gender
significant
sgrificant
Figure 2: Fairness correlation results for each explanation method. Occlusion- and 1.2-based expla- nations are effective for bias detection across different bias types and models.
Results Figure 2 shows that the best-performing explanation methods, such as Grad 1.2, IxG L.2, DeepLift L2, Occlusion, and Occlusion abs, generally achieve high fairness correlations across dif- ferent models and bias types, indicating a strong ability to detect biased predictions. Besides, their fairness correlations are mostly statistically significant (p group categories, which confirms their reliability. Among these methods, Occlusion and Occlusion abs perform best with BERT models, whereas the L2-based methods Grad L2, IxG L2, and DeepLift L. are most effective with Qwen3-4B.
0.05) in all, or in all but one, class-
When comparing different variants of the same explanation family, mean-based approaches perform considerably worse than their 1.2-based counterparts, and also underperform compared to undirected attention-based methods. We attribute this limitation to their dependence on accurately determining the direction of each token's contribution, a challenge that attention- and L2-based explanations do face. Our analysis in Appendix further shows that the effectiveness of explanation- based bias detection is not determined by explanation faithfulness. underscoring the need for careful evaluation when selecting methods for bias identification. In addition, our human study Appendix J shows that explanation-based signals can assist human faimess auditing.
Takeaway: Input-based explanation methods, particularly Occlusion- and bas-b ones, are ef- feetive for identifying biased predictions at inference time.
4.3 RQ2: EXPLANATIONS FOR MODEL SELECTION
Given that explanations can detect biased predictions (RQI), we next investigate whether they can also be used to select fair models among candidates. Prior work has demonstrated that input- based explanations on validation examples can help humans identify spurious correlations in mod- els (Lertvittyykkumjom & Toni 2021 Pezeshkpour al. 2022). Extending this idea, examine whether explanations can be leveraged for automatic fair model selection, thereby removing the need for human intervention.

---

Published as a conference paper at ICLR 2026
Our experiments consist of three steps: (1) for all default and debiased models (seven encoder- only and four decoder-only), we generate predictions on a validation set and compute explanation- based metrics; (2) compute fairness metrics the test set for each model; and (3) we evaluate model selection ability using two measures: Spearman's 's rank correlation (p) between validation set explanation-basee baseed metrics and test fairness metrics, which reflects the ability to rank models, and mean reciprocal rank of the fairest model (MRR @ 1), which reflects the ability to select the fairest model. Higher rank correlations and MRR @ indicate that explanation method useful for ranking models and selecting the fairest one. Specifically, we use the average absolute sensitive token reliance on the validation on average individual unfairness the test set
the explanation- metric to rank and select models based
BERT
1.3
Vel faimess [baseline]
Bank Coree
0.5
roliout
Alnn flow
Grad
IxS meon
Gender
IGG L2
IntGrad
Qwen3-48
1.2
Deepl mean
Pank Convelation
DeepLift
Occlusion
Ooclution
KemeiSHAP
Decompx
Proginfer
Race
Gender
Religron
Figure 3: Rank correlations between validation set average absolute sensitive token reliance and test set individual unfairness. The validation set sizes are 500 for race and gender, and 200 for religion. None the explanation methods consistently achieve performance on par with the baseline.
Results As baseline, we report results of using the validation set average individual unfairness the predictor of test set fairness performance. The results are averaged over six and three random validation set selections for encoder- and decoder-only models, respectively. Results for more models and the Jigsaw dataset are presented Appendix
Vel Parmene chase Ine)
fancomn
Allontion
NG
Owrea
insiaa
intrred 13
Deepult main
Dtep
Ortueion Ocdlasion
The results in Figures and highlight the limi- tations of using explanations for model selection. Although some methods occasionally show high rank correlations (e.g., Grad 1.2 for race and reli- gion biases in BERT and Acclusion-based meth- ods for gender and religion biases in Qwen3-4B), none of them consistently reach the baseline of using the individual unfairness on the validation set. This limitation is particularly evident in decoder-only models, where the baseline achieves a perfect rank correlation of 1. Similarly, the baseline consistently achieves the highest MRR@ scores, fur- ther showing the limited effectiveness of explanation- methods in selecting the fairest models. Considering that these explanations are often more computationally expensive to generate than eval- uating validation set fairness, they we do not recommend explanation- based model selection, especially in decoder-only models. The difference in findings between RQ1 and RQ2 may stem from the fact that debiasing methods alter model behaviors and thereby affect explanation attributions. As a result, comparing explana-
Figure 4: Average MRR @ across bias types. Explanation methods perform worse than the baseline in identifying the fairest models.
practically useful model fairness indicator. Therefore,
We have evaluated other metrics predict group fairness outcomes. However, neither explanation based metries nor validation set fairesss achieved rank correlations beyond random chance with the test set results. The full set of evaluated metries provided in Appendix

---

Published as a conference paper at ICLR 2026
tions across default and debiased models is less reliable, whereas comparing explanations within the same model remains effective for detecting biased predictions.
Takeaway:
Input-based explanation methods not reliable tools for selecting fair models.
4.4 RQ3: EXPLANATIONS FOR BIAS MITIGATION
Having shown that explanations can reliably reveal biased predictions (RQ1), we now investigate whether they can also leveraged mitigate model bias. Building on prior work demonstrating that explanation regularization can reduce spurious correlations while also improving performance and generalization (Kennedy et al., 2020; Rao et al., (023), we investigate bias mitigation by min- imizing sensitive token reliance during training. Following Dimanov al. (2020), define debiasing regularization term, Leehias which penalizes the average sensitive token reliance of all such tokens input, in addition the task loss:
L == Ltask + @LLdebias
Here, a is a hyperparameter that controls the strength of sensitive token reliance reduction. For embedding-level attributions, we apply either an LI or L2 norm penalty, corresponding minimiz- ing mean- or L2-based reliance scores, respectively.
While Dimanov al. (2020) tune hyperparameterss based on task accuracy, we search a (0.01, 1, 10, ] using fairness-balaneee metric (the harmonic mean of accuracy and 100-un- fairness) on the validation se Models are selected separately for each fairness criterion and results are averaged over three runs. Due to computational cost, we restrict training to single bias types. We exclude DeepLift, DecompX, and KerneISHAP, as they are not easily differentiable and thus cannot be incorporated into model training. Integrated Gradients is substantially more expensive in time and memory for generating explanations and tracking gradients, sp we apply them only to race bias mitigation in BERT and report the results in Table in Appendix G More implementation details are provided Appendix B
Disp
Dispiy
HOLL HGLD Demuns
Mtantom
goot
natoot
dow
cool Goof
b012
Destaion
Actetton
ritort
NOLL Demmus
Gonder
Actestion
KOL
Attetion
poose tove
u and WO
Actetion
thst
Lod
Lo
Decistion
Attentine
pnter fow
L.
croa
Accurae
Origial
Farmic
Figure 5: Accuracy and fairness results for bias mitigation using different explanation methods. Each column corresponds models selected by maximizing the faimess-balanced metric with spect to the indicated bias metric. We find that explanation methods can improve fairness across many metrics while maintaining reasonable task accuracy.
As Occlusion is sensitive the dehiasing strength, we E {0.002, 0.004, 0.006, 0.008, 0.01).
8

---

Published as a conference paper at ICLR 2026
Results In Figure 5] we present race and gender bias mitigation results. For consistency with accuracy, fairness results are reported as 100-(DisPacc: Disp/pr: Disp A. so that higher values indicate lower bias. We find that explanation- baasid bias mitigation effectively improves fairness across multiple metrics. Most notably, it consistently and substantially reduces Dispin for all bias types. For gender bias, it also yiclds considerable reductions in Dispase and Avg.. is mitigated for race bias. Moreover, as shown in Figure all group fairness disparity metrics decrease for religion bias. The bias mitigation effects are consistent across all models and are also observed on the Jigsaw Appendix G).
dataset (see Figures 19 20 21
At the same time, explanation based debiasing maintains a good balance between fairness and accu- racy. For example, Grad I.1 both increases accuracy and reduces Dispace firp and Avgn, for gen- der bias, while most other explanation methods also achieve better Dispace and Disp inc with marginal or no accuracy loss. Our harmonic confirm this by showing that explanation-base debiasing almost always achieves comparable or higher harmonic means than both default models and traditional debiasing methods.
mean results (Figures 23 24 25 26 further
Among individual explanation methods, attention and atin flow achieve strong debiasing perfor- mance on BERT, while IxG L1 and L2 consistently yield a good balance between accuracy and fair- ness across models. Overall, IxG 1.2 and attention-based methods provide robust debiasing while maintaining favorable carness-accurass flected in the harmonic mean results. Our findings differ from those Dfmanooo et al. (2020). which we attribute to our fairness-basee hyperparameter tuning strategy. We further show in Appendix K that combining traditional and explanation-basee debiasing methods outperforms either approach in isolation.
trade-off across bias types, models, and datasets, as re-
Takeaway: Input-based explanations can provide effective supervision for mitigating model bias during training while maintaining a good fairness performance trade-off. In particular, IxG L.2 and attention-basee methods achieve robust debiasing with strong overall balance.
Yairnes--perfrrmncce
5 BIAS DETECTION IN DEPLLNNATIONDEBIIAE
MODELS
While explanation- based methods are effective in reducing bias (RQ3), their suppression of attributions on
Altondo--034
Alts fow
1022 6:22
8:17
014
0.00 5.16 537 30 0.23
5.004 (019 A11
sensitive tokens could potentially mislead users into believing that
Dobleeng Mattod (R23)
Aitn
GrddL] 6.21
0.21
6.66 014 4.12 5.55 0.051 0.224 0.944
model predictions are unbiased (Di-
manov et al.] 2020 [llakk et al.] 2020 Pruthi et al.] (020)). To inves-
Grad12 0.23 4.13 4.000.0000003 012
-312 G61 2012
0.345 0.22 0.02
4.27
0.13
032
bG 6.35 4.29 0.29 4.003
032
0079
tigate this concern, we reapply the bias detection procedure from RQI to explanation-debbased models and compare their fairness correlations with those from the corresponding default models. For this analysis, we use the models debiased for race bias with respect to individual unfairness, as described in RQ3.
SGL2 0.23 4.28 4:33 2.008 .218 548 0.324 221 0.05 -.21
095 035
-04
Occusion 6.32 41 3.000 4.00 -31 0.3 -822 012 0.002
4.17
082 012
Antertion
Antr edoii
Asts flow
man
Gradt
ledi mann
L2
Ocokstion
dectunion. aby
Dersuft mean
Desplat La
nottoel Kometoauk
-66
Exp onetion Method PRO
Figure 6: Fairness correlation differences between de- fault and explanation-debiased BERT. Occlusion- and L2- based explanations (except IntGrad L2) are less affected by explanation- based debiasing and remain effective for bias detection.
The fairness correlation differences from default models are shown in Figure We observe that the impact of explanation-basee debiasing on fairness correlations depends on both the explanations used for debiasing and those used for bias detection. Some approaches, such as Grad mean/l.2, IxG L2, DeepLift mean/L2, Occlusion, and Occlusion abs, are only marginally, or even positively, affected by debiasing. Their faimess correlation scores (see Figure 32 in Appendix L further indicate that Occlusion- and L.-based methods (except IntGrad L2) remain reliable for revealing bias in -ebiased models. In contrast, attention- based explanations experience substantial drops, particularly when the models themselves are debiased using attention-based methods. Similarly, InGGrad-based explanations show reduced bias detection ability when the debiasing procedure is

---

Published as a conference paper at ICLR 2026
also gradient-based. Overall, these findings demonstrate that certain input-based explanations main effective for detecting biased predictions even in iiltaaationdebiasee models. Our results different from those of Dimanov al. (2020), likely because their analysis focused solely on attribution magnitudes without considering their relationship to fairness metrics.
EXPLANATION-BASS BIAS DETECTION vs. LLM-AS-A-JUDGE
Existing research suggests that LLMs could identify and correct biased model outputs (Bai et al. 2022 Furniturewala et al., 2024). In this section, we compare the bias detection ability of input- based explanations against LLMs' judgments under two paradigms: (1) LLM decision, where LLMs are asked to indicate whether model's prediction rely on bias or stereotypes, and (2) LLM attri- bution, where LLMs choose a K-word rationale from the input, which we then examine for the presence of sensitive tokens. We conduct this analysis using two LLMs, Qwen3-4B and GPT-OSS- 120B, on predictions made by Qwen3-4B on the race subset of Civil Comments (see Appendix B for the prompts used).
Table 2 shows the results of juue for bias detection. Under the LLM decision setup, Qwen3-4B is extremely conservative: it flags only 86 out of 4000 predictions biased, and all of them correspond to toxic predictions. Moreover, the predictions labeled as biased by the model ex- hibit lower average individual unfairness than those labeled non-biased, indicating poor precision as well. Under LLM attribution, Qwen3-4B performs slightly better: predictions whose rationales contain sensitive tokens show higher average individual unfairness than those without. However, this still falls short of a simple input-based explanation baseline that flags the top 50% of predictions ranked by absolute Grad 1.2 reliance scores (Grad 1.2 Binary). The larger GPT-OSS- 120B exhibits improved bias detection ability in the LLM decision setting. but its performance under L.MM attribu- tion remains comparable to Qwen3-4B and still substantially worse than input-based explanations. Overall, we conclude that input-based explanations detection. This finding aligns with the observations of Yang et al. (0225)) who also report that LLM-as---judge- is unreliable for bias detection.
more reliable than LLM-as-a-judge for bias
Table 2: Results of LLM-as-a-judge for bias detection using Qwen3-4B and GPT-OSS-I20B. Predictions come from Qwen3-4B on race-related Civil Comments examples. "Biased/Unbiased" denotes whether an example is judged as biased or unbiased by the LLM through LLM decision or LLM attribution. If the judgments are reliable, Avgi should be higher for biased examples than unbiased ones. For LLM decision with Qwen3-4B, faimess correlation cannot be computed because the model labels no non-toxic predictions as biased. Input-based explanations reveal bias more reliably than jude-ee-a
LLM
Method
Binsed/Unbiased Avg. (Biase(UUubiise))
Fairness Correlation
LLM decision
$6/3914
0,065/2.59
Qwen3-4B
I. M attribution (K-5)
20633/1944
3.55/1149
0.104
LLM attribution (K=10)
2176/1474
2.93/1.56
0.070
LLM decision
399/3601
4,42/2.35
0:051
GPT-OSS-120B
LLM attribution (K=5)
2153/1843
3.33/1.65
L ultribution (K-10)
2729/1238
2.88/1.74
0.063
Grad L2 Binary
2000/2000
5.02/0.09
0,194
7 CONCLUSION
In this work, we present the first comprehensive study linking input- explanations and fairness in hate speech detection. Our experiments show that (1) input-basce explanations effectively identify biased predictions, (2) they are not reliable for selecting fair models, and (3) they can serve as effective supervision signals during training. mitigating bias while preserving strong balance between fairness and task performance. We further provide practical recommendations on which explanation methods are best suited for bias detection and bias mitigation. Finally, our analyses demonstrate that explanation-based bias detection remains effective in explanation-deba models, and they outperforms LLM auudde- in identifying biased predictions
'I Limitations and future directions discussed in Appendix M
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
This work investigates explainability and fairness in hate speech detection. Despite the diverse experimental setups explored and the additional generalization tests in Appendix H the findings are still constrained by the specific configurations considered here. As such, the results may not fully generalize across demographic groups. domains, or tasks, and they may remain vulnerable to adversarial manipulation. We further caution that explanation methods and debiasing techniques cannot fully eliminate residual harms, and that LLM-generaeed bias judgments are unreliable for bias detection. We hope that study will contribute to the development of NLP systems that more transparent, reliable, and fair.
REPRODUCIBILITY STATEMENT
We include full implementation details in the main text and appendix, covering data pre-processing details, model architectures, training procedures, and hyperparame- ters. Our code and scripts for data pre-processing and experiments are available at https//ggthabccommEEvawvonnnnnns x explainability
ACKNOWLEDGEMENTS
This work was funded by the DFG project GRK 2853 "Ncurocxplicit Models of Language, Vision, and Action" (project number 471607914).
We gratefully acknowledge the computing time made available to them on the high-performance computer the NHR Center of TU Dresden. This center is jointly supported by the Federal Ministry of Research, Technology and Space of Germany and the state governments participating in the NHR www.ahn-verrinn.dd/uusseeeeeeep
Mayank Jobanputra supported by Deutsche Forschungsgemeinschaatt (DFG, German Research Foundation) Project ID 389792660-TRR 248 (Foundations of Perspicuous Software Systems).
We also sincerely thank the reviewers and area chairs for their exceptionally detailed and construc- tive feedback.
REFERENCES
Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Dan Ju- rafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.), Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pp. 4190-4197, Online, July 2020. Association for Computational Linguistics. doi: 20.18653/v1/2020.0 m.ai.......maa...8....... URL
in..mnnn.. 385/|
Leila Arras, Ahmed Osman, Klaus-Rohert Müller, and Wojciech Samek. Evaluating recurrent neural network explanations. Tal Linzen, Grzegorz Chrupal a. Yonatan Belinkov, and Dieuwke Hup- kes (eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 113-126, Florence, Italy, August 2019. Association for Compu- tational Linguistics. doi: 10.18653/v1/W19-444
URL https::/aclaatthooogg .org
W19 079-481377
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. Diagnostics- guided explanation generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp. 10445-1044 2022.
Giuseppe Attanasio, Debora Nozza, Dirk Hovy, and Elena Baralis. Entropy-based attention reg- ularization frees unintended bias mitigation from lists. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (eds.), Findings of the Association for Computational Linguistics: ACL 2022, pp. 1105-1119, Dublin, Ireland, May 2022. Association for Computational Linguis- tics. doi: 10. 00.18653/v1/0022 1/20.:3/i//.2...mdinnn...... acl.88. URL https: http:::://.aaaattoooooo.oooo/ Findings-ace .88/

---

Published as a conference paper at ICLR 2026
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Rebur- Müller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130400, 2015.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In Yoshua Bengio and Yann LeCun (eds.), 3rd International Conference on Learning Representations, ICLR 2015. San Diego, CA, USA, May 7-9, 2015, Con- ference Track Proceedings, 2015. URL httt:://aatttii org abs 11199449.0000 0473
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Ols- son, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran- Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosiute, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemí Mer- cado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer EI Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Con- erly. Tom Henighan, Tristan Hume. Samuel R. Bowman, Zae Hatfield-Dodds. Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. Constitutional Al: harmlessness from AI feedback. CoRR, abs/2212.080733. 2022. doi: 10.48550/ARXIV.2222. 08073. C https: //doi woo. 48550/855///0 221 2212. 08073
Esma Balkir, Svetlana Kiritchenko, Isar Nejadgholi, and Kathleen Fraser, Challenges in applying explainability methods to improve the fairness of NLP models. In Apury Verma, Yada Pruk- sachatkun, Kai-Wei Chang. Aram Galstyan, Jwala Dhamala, and Yang Trista Cao (eds.), Pro- ceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLPP 2022), pp. 80-92, Seattle, U.S.A., July 2022, Association for Computational Linguistics. doi: 10. 18653/ v1/2022. ttrpr22ltuuunnp--1p 1.8. URL
Vaishnavi Bhargava, Miguel Couceiro, and Amedeo Napoli. Limeout: An ensemble approach to improve process fairness. In Joint European conference on machine learning and knowledge discovery in databases, pp. 475-491. Springer, 2020.
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. Language (technology) is power: A critical survey of "bias" in NLP. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.), Proceedings of the 58th Annual Meeting of the Association for Computa- tional Linguistics, pp. 5454-5476, Online, July 2020. Association for Computational Linguis- URL https: http::///aaaaahhhhaaaamoa letcchlooy..o org/2020. ontheelory.orr//202..
tics. doi: 1..110656300 20t33//112220..l-mmiin sellmainn444 485/
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to computer programmer as woman to homemaker? debiasing word embeddings. Advances in neural information processing systems, 29, 2016.
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie Chen, Kathleen Creel, Jared Quiney Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Dur- mus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt Daniel E. Ho, Jenny Hong. Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keel- ing, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Ku- ditipudi, and et al. On the opportunities and risks of foundation models. CoRR, abs/2108.07258, 2021. URL https: http://aaxvvv.... org/ abs 200. 07258
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Nuanced metrics for measuring unintended bias with real data for text classification. In Companion pro- ceedings of the 2019 world wide web conference, pp. 491-500, 2019.
Aylin Caliskan, Joanna Bryson, and Arvind Narayanan. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334))183--1883 2017.
12

---

Published as a conference paper at ICLR 2026
Yuen Chen, Vethavikashini Chithrra Raghuram, Justus Mattern, Rada Mihalcea, and Zhijing Jin. Causally testing gender bias in LLMs: case study on occupational bias. In Luis Chiruzzo, Alan Ritter, and Lu Wang (eds.), Findings of the Association for Computational Linguistics: NAACI, 2025, pp. 4984-5004, A Albuquerque, New Mexico, April 2025. Association for Compu- URL
tational Linguistics. ISBN 979-8-99177-1195--7 doi: 10.18653/v 1001866331112225555
httg02222...dg--aaaaaa.a naacl. naacl.2817
ejadams, Daniel Borkan, inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and nithum. Jigsaw unintended bias toxicity httpa/iccciiioo..tsssss///aaeeee compet itions/ Digsaw wnw.unnneeeeeddie httpeaaoooootttctooooaaaaaaaaaaa 2019 Kaggle.
Benjamin Cohen- Cohen-Wang, Harshay Shah, Kristian Georgiev, and Aleksander Madry. Con- textcite: Attributing model generation to context. In A. Globerson, L. Mackey, D. Bel- grave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances Neural In- formation Processing Systems, volume 37, pp. 95764-95807. Curran Associates, Inc., 2024. URL https::/procceeeeeeee neurips http:ipppccppppppeeeeeee forerr__lleee http:/eerrreieeeeeap/ee//// 2211:000001122222000033333366662
pdf
Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, and Morteza Dehghami. Fair hate speech detection through evaluation social group counterfactuals. 12779
CoRR, abs/2010. 2020. URL
org. wtt./org///////oooo
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech de- tection and the problem of offensive language. Proceedings of the International AAAI Conference on Web and Social Media, 11(1):512-515, May 2017. doi: 10. wi httpps:///// aaai org/ indx php/ ICSM// art eeveeeeiiieeee http:///r.....leeeeeee//1155.55
URL
Luca Deck, Jakob Schoeffer, Maria De-Arteaga, and Niklas Kühl. A critical survey fairness benefits of explainable ai. In Proceedings of the 2024 ACM Conference on Fairness, Account- ability, and Transparency, FAccT '24, pp. 1579-1595, New York, NY, USA, 2024. Associa- tion for Computing Machinery, ISBN 9798400704505. doi: 10. 10.145//630106.366899, URL https://doi.org:
1111/113111111 3658990
Björn Deiseroth, Mayukh Deb, Samuel Weinbach, Manuel Brack, Patrick Schramowski, and Kristian Kersting. memory efficient attention manipulation. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.). Advances in Neural Information Pro- cessing Systems, volume 36, pp. 63437-63460. Curran Associates, Inc., 2023. URL wwwes//ppeeee///ee//ee/a file/ paf
Atman: Understanding transformer predictions through
wwwpiggs..eeuuiiee nwwrins cc.pspe.ccppppppeeee. https::/prroceednns...neet c8 e3Jc02062200e3336e10066466666663
Jacob Devlin, Ming-Wos Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio (eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume I (Long and Short Papers), pp. 4171-4186 Minneapolis, Minnesota, June 2019, Association for Com- putational Linguistics. doi: 10. 1111888/1//11911420 URL hacep:://aclanthhlooyy.
N19-1423/
Jay De Young, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. ERASER: A benchmark to evaluate rationalized NLP models. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.). Proceedings of the 58th Annull Meeting of the Association for Computational Linguistics, pp. 4443-4458, Online, July 2020. Association for Computational Linguistics. doi: 10. 10.1880 11653/v1/2020.cooo acl- main.40s URL https: //aaclnthholoyy. www.olloyyy.gg//2220 wwt.or///2220 2020 acc-main..ee.
Botty Dimanov, Umang Bhatt. Mateja Jamnik, and Adrian Weller. You shouldn't trust me: Learning models which conceal unfairness from multiple explanation methods. In ECA/ 2020, pp. 2473- 2480. IOS Press, 2020.
13

---

Published as a conference paper at ICLR 2026
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Measuring and mit- igating unintended bias in text classification. In Proceedings of the 2018 AAAI/ACM Confer- ence AI. Ethics, and Society, AIES 18, pp. 67-73, New York. NY, USA, 2018. Associa- URL
tion for Computing Machinery. ISBN 978 9781450360128 doi: 10.1145/3278721 htttps://do..orst
111111175772222 3278729
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang. Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang. Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurélien Rodriguez. Austen Gregerson, Ava Spataru, Baptiste Rozière, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Ander- son, Graeme Nail, Grégoire Mialon, Guan Pang. Guillem Cucurell, Hailey Nguyen, Hannah Ko- revaar, Hu Xu. Hugo Touvron, Iiyan Zarov, Imanol Arrieta Ibarra, Isabel M. Kloumann, Ishan Misra, Ivan Evtimov, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Ma- hadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Al- wala, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, and et al. The Ilama herd of models. CoRR, abs/2407 07.44477.,7 2024. doi: 1044550/ARXIV.2407.22 petps://doo wwtp////.../////////////
URL
48550/ wttp://rxiiv.....0
21783
Joseph Enguehard. Sequential integrated gradients: simple but effective method for explaining language models. In Anna Rogers, Jordan Boyd-Grabes and Naoaki Okazaki (eds.), Findings of the Association for Computational Linguistics: ACL 2023, pp. 7555-7565, Toronto, Canada, July URI.
2023. Association for Computational Linguistics. doi: 10. 1200333/111222..ddnnggs... ogy. org/2023. nndingg-- acl 477/
/ttps://aclaaniee
James Enouen, Hootan Nakhost, Sayna Ebrahimi, Sercan Arik, Yan Liu, and Tomas Pfister. TextGenSHAP: Scalable post-hoc explanations in text generation with long documents. In Lun- Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Findings of the Association for Compu- tational Linguistics: ACL 2024, pp. 13984-14011, Bangkok, Thailand, August 2024, Associ- ation for Computational Linguistics. doi: 10. 10.18633/v1/001 1/t.p4. ftw.:2244..niingaal...... //aclantholoog..zz//2220
URL https:
findings-ncllli 8327
Xiao Fang. Shangkun Che, Minjia Mao, Hongzhe Zhang, Ming Zhao, and Xiaohang Zhao. Bias of ai-generated content: examination of news produced by large language models. Scientific Reports, 14(1):5224, 2024.
Javier Ferrando, Gerard I. Gállego, and Marta R. Costa-jussà, Measuring the mixing of contextual information in the transformer. In Yoav Goldberg. Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp- 8698 3714, Abu Dhabi, United Arab Emirates, December 2022. Association Computational Linguistics. doi: 10. 18633// 00533//1/2022.mmm w/wp:22emmmlpmmain..... 2022. emnlp mammannn... 5957
org/
Paula Fortuna and Sérgio Nunes. A survey on automatic detection of hate speech in text. ACM Comput. Surv. 51(4), July 2018. ISSN 0360-0300.
doi: 10.1145/3232676. URL https:
7/doi. 10. 1145/32326777
Shaz Furniturewala, Surgan Jandial, Abhinav Java, Pragyan Banerjee, Simra Shahid, Sumit Bha- tia, and Kokil Jaidka. "thinking" fair and slow: On the efficacy of structured prompts for debiasing language models. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, tics. doi: Miami, Florida. USA. November 2024. Association for Computational Linguis- 1ttp3//112222..mpmmmmo eemn 1-mmapmmaa ma i n. 13/
pp.
URL https: /tt///caantttt http://aaaannnatt..aaaaaaooo wrgp/o/...rg20000.
14

---

Published as a conference paper at ICLR 2026
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Der- noncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. Bias and fairness in large lan- guage models: A survey, Computational Linguistics, 5063::097--1179 September 2024. doi: 10.1162/coli a 00524. htt.iittsss//cclattooooooo...
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Fos- ter, Laurence Golding, Jeffiry Hsu, Alain Le Noac"h, Haonan Li, Kyle McDonell, Niklas Muen- nighoff, Chris Ociepa, Jason Phang. Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lin- tang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. A framework for few-shot language model evaluation, 12 2023. URL httpstts:://eet htt::://eeooooooo wttp/r/crrreeeee 10256836
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. Counter- factual fairness in text classification through robustness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Sociery, AIES 19. pp. 219-226, New York, NY. USA, 2019. Asso- ciation for Computing Machinery, ISBN 9781450363242. doi: 02L144//300661833199955 URL fttt::://doi.org/11 11.....111133330 .. 3317950
Santiago González-Silot, Andrés Montoro-Montarroos Eugenio Martínez Cámara, and Juan Gómez-Romero. Enhancing disinformation detection with explainable AI and named entity re- placement. CoRR, abs/2502 044663, 2025. doi: 10.4850/ARXIV.500..4555 URL https:
2502 04863
Przemyslaw Grabowiez, Nicholas Perello, and Aarshee Mishra. Marrying fairness and explain- ability in supervised learning. In Proceedings of the 2022 ACM Conference on Fairness, Ac- countability, and Transpurency, FAccT '22, pp. 1905-1916, New York, NY, USA, 2022. Associ- ation for Computing Machinery. ISBN 9781450393522. ht http:::/ddo..ogg///// 114./ 3331145531166... 3533236
doi: 10. 10.1145/3331 URL
Vipul Gupta, Pranav Narayanan Venkit, Hugo Laurençon, Shomir Wilson, and Rebecca Passon- neau, CALM : A multi-task benchmark for comprehensive assessment of language model bias. In First Conference on Language Modeling, 2024. URL//ttpss//gpeneerreew... forum?
Rd=RLFaaarx
James M Hickey, Pietro G Di Stefano, and Vlasios Vasileiou. Fairness by explicability and adversar- ial shap learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 174-190. Springer, 2020.
Alon Jacovi and Yoav Goldberg. Towards faithfully interpretable NI. systems: How should we define and evaluate faithfulness? In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.). Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4198-4205, Online, July 2020. Association for Computational Linguistics. doi: 18.15v///v// 2020.acl- m3...386. URL htttps://allathhhl
org. 202C. acl mall-mainn... mww.maiiinn.........
Sarthak Jain and Byron C. Wallace. Attention is not Explanation. In Jill Burstein, Christy Doran, and Thamar Solorio (eds.), Proceedings of the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, Volume (Long and Short Papers), pp. 3543-3556, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10. 10.18653/v1/N19-115 1357. URL
org/N19-133 C
Manuela Jeyaraj and Sarah Delany. An explainable approach to understanding gender stereotype text. In Agnieszka Faleńska, Christine Basta, Marta Costa-jussă, Seraphina Goldfarb-Tarrant, and Debora Nozza (eds.), Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pp. 45-59, Bangkok, Thailand, August 2024. Association for Compu- tational Linguistics. doi: 10. 18153// 2etp://p22...elp1.....4 URL ittps://aclantholl.o orq/2024 2024 1e.
Faisal Kamiran and Toon Calders. Data preprocessing techniques for classification without discrim- ination. Knowledge and information systems, 13-1); 2012.
15

---

Published as a conference paper at ICLR 2026
Sanjay Kariyappa, Freddy Lecue, Saumitra Mishra, Christopher Pond, Daniele Magazzeni, and Manuela Veloso. Progressive inference: Explaining decoder-only sequence classification models using intermediate predictions. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.). Proceedings of the s International Conference Machine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 23238-23255 PMLR, 21-27 Jul 2024. URL https: httpss::pppcceednngge mmr..
R html
Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, Morteza Dehghani, and Xiang Ren. Con- textualizing hate speech classifiers with post-hoe explanation. In Dan Jurafsky, Joyce Chai, Na- talie Schluter, and Joel Tetreault (eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5435-5442, Online, July 2020. Association for Computational org/
Linguistics. doi: 10.18653/11/2020 wtt.:/20...-maai..44..4 main.
URL attps://aclantheen
1020.acl-mon
483/
Jiyun Kim, Byounghan Lee, and Kyung-Ah Sohn. Why is it hate speech? masked rationale pre- diction for explainable hate speech detection. In Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Do- natelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, and Seung-Hoon Na (eds.), Proceedings the 29th International Conference on Computational Linguistics, pp. 6644-6655, Gyeongju, Republic of Korea, October 2022. International Committee on Computational Linguis- Linguus tics. URLhttpss: / /aaaaaaaaattooooooooooo wtt.oo/gg///// coliigg-.5777
the
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne. Investigating the influence of noise and distractors on the interpretation of neural networks. CoRR, abs/ 161 1.02270, 2016. URL [tttp://aaaiii wwwpp/iwwwrrraa///ooraoo.oa.aaa 111//111111110 C7270
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi- An evaluation of the human-interprettbiiiie of explanation. CoRR, abs//192.0000 2019. URL/attpp//arxiv www.vvv.rrrggbeebeeeeeeee.oeese C0006
Piyawat Lertvittayakumjorn and Francesca Toni. Explanation--assed human debugging of NLP mod- els: A survey. Transactions of the Association for Computational Linguistics, 9: 9:1508-1528, 2021. doi: 10. 00440. URL htttttts://ccaaatoooooooooo/ httpl//oggyrggg222/2///// tacl-1llll11 907]
Jiwei Li, Will Monroe, and Dan Jurafsky. Understanding neural networks through representation 01220000000
erasure. CoRR, abs/1612.08220,, 2016. URL
Yan Liu, Yu Liu, Xiaokang Chen, Pin-Yu Yu Chen, Daoguang Zan, Min- Yen Kan, and Tsung- Yi Ho. The devil is in the neurons: Interpreting and mitigating social biases in pre-trained language models. CORR, abs/2406.10111 10130, 2024. doi: 10.48550/ARXIV.240006 10130. URL httpps://
doi omg.orrg10:0. 48w50/85555500
2406 10130
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized BERT pretraining abs / 907 11692]
approach. CORR, abs/1907. 1692, 2019.
Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Gar- nett (eds.), Advances in Neural Information Processing Systems, volume 30, pp. 1-10. Cur- ran Associates, Inc 2017. URL /tpp:tsss///rooceiiinssssss wew.eeeedddnnn..nn.eeeeeeeeeeeee cc.ppprr.cppppp.pppppppp. file// 80 (1222119762227776448488 paper/2017/Er http:///..........2222222222232.
Qing Lyu, Marianna Apidianaki, and Chris Callison-Burch Towards faithful model explanation in NLP: A survey. Computational Linguistics, 50(2):657-723, June 2024. doi: 10.1162/coli 00511. URL http:::t////cccttc anthholllnnanocccoooaoaa wwg.ooooooooooo..
Andreas Madsen, Sarath Chandar, and Siva Reddy. Are self-explanations from large language mod- els faithful? In Lun-Wet Ku, Andre Martins, and Vivek Srikumar (eds.), Findings of the As- sociation for Computational Linguistics: ACL 2024, pp. 295-337, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10. 1...18853/11/2244 fieee...ininngaaa. ac.. URL https: //acl antho wg..ooo.o....................... lgt. ndi wndinninnnsann 197
16

---

Published as a conference paper at ICLR 2026
Mamta Mamta, Rishikant Chigrupaatii, and Asif Ekbal. BiasWipe: Mitigating unintended bias in text classifiers through model interpretability. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.). Proceedings of the 2024 Conference on Empirical Methods in Natu- ral Language Processing, pp. 21059-21070, Miami, Florida, USA, November 2024. Associa- tion for Computational Linguistics. doi: 10. 00065///1/224.mmllpp/ee 7 //aclanthlooyy.org wwwpoooogyyoogooooaoooooo wwwg///g/......... wwn.pmmmainn11177
1172. URL https:
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh
Mukherjee. Hatexplain: A benchmark dataset for explainable hate speech detection. Proceedings of the AAAI Conference on Artificial Intelligence, http://nnnnnnne.aaa///aaaaaaaaaa (60%/aaai.v3511..7777 v35117.177455. BRLhhtts:://o]] http::://ossaaaaiaa......aa org index
May 2021. doi: 10.
view
Chandler May, Alex Wang, Shikha Bordia, Samuel Bowman, and Rachel Rudinger. On measuring social biases in sentence encoders. In Proceedings of the 2019 Conference of the North Ameri- can Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 622 628, 2019.
Chuizheng Meng, Loe Trinh, Nan Xu, James Enouen, and Yan Liu. Interpretability and fairness evaluation of deep learning models on mimic-iv dataset. Scientific Reports, 12(1):7166 2022.
Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh, and Mohammad Taher Pilehvar. GlobEnc: Quantifying global token attribution by incorporating the whole encoder layer in trans- formers. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (eds.), Proceedings of the 2022 Conference of the North American Chapter of the Association for Com- putational Linguistics: Human Language Technologies, pp. 258-271- 271, Seattle, United States, July 2022. Association for Computational Linguistics. doi: 10.18653/v htwp//222222n2nmmnnmm -aaimmmii.. 19. URL https: /aa::://ccaaaa hataaaccannhhooggg.o org. 2022. naacl ma iiaain.. 197
Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh, Yadollah Yaghoobzadeh, and Moham- mad Taher Pilchvar. DecompX: Explaining transformers decisions by propagating token de- composition. In Anna Rogers. Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Proceedings of the ō1st Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 2649-2664, Toronto, Canada, July 2023. Association for Computational Linguis- 2023.
tics. doi: 10. 1/tpp1..2022-22el1 149. URL https:
ac woo.ng..10. 1497
Fahim Muntasir and Jannatun Noor. Explainable ai discloses gender bias in sexism detection algo- rithm. Proceedings of the 11th International Conference on Networking, Systems, and Secu- rity, NSysS '24, pp. 120-127 New York, NY, USA, 2025. Association for Computing Machin- ery. ISBN 9798400711589 doi: 10.1145/3704522.774422. URL (tRl:htts:: htthhs:sss//....t../// sordoiioorggC...
1145/370 3704524
Ayushi Nirmal, Amrita Bhattacharjee, Paras Sheth, and Huan Liu. Towards interpretable hate speech detection using large language model-extracted rationales. In Yi-Ling Chung, 7eerak Talat, Deb- ora Nozza, Flor Miriam Plaza-del Arco, Paul Röttger, Aida Mostafazadeh Davani, and Agostina Calabrese (eds.), Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024), pp. 223-233, Mexico City, Mexico, June 2024. Association for Computational Linguistics. doi: 10. 18653/v1/2024 woah- 1.17. URL https: www.acannnnooyy.... org/
org/ 2024 wwah- 17/
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. Abusive lan- guage detection in online user content. In Proceedings of the 25th International Conference on World Wide Web, WWW '16, pp. 145-153, Republic and Canton of Geneva, CHE, 2016. International World Wide Web Conferences Steering Committee. ISBN 9781450341431. wtt..o///....11/25225555555
doi:
10.1145/2872427.2888888 2883062..
010.0..11111888818 011444/22722222288855555 2883062
Ji Ho Park, Jamin Shin, and Pascale Fung. Reducing gender bias in abusive language detection. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun minichii Tsujii (eds.), Proceedings of the 2018 Conference Empirical Methods in Natural Language Processing, pp. 2799-2804, Brussels, Belgium, October-Nor Noveebr 2018. Association for Computational Linguistics. doi: 10.18653/1//11-133000 (tttttsts:://ac h htw.oaaatoooooooooooooooooo 00t0h////1///////1133320
17

---

Published as a conference paper at ICLR 2026
Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, and Byron Wallace. Combining feature and in- stance attribution to detect artifacts. In Smaranda Muresan, Preslav Nakov, and Aline Villavicen- cio (eds.), Findings of the Association for Computationnal Linguistics: ACL 2022. pp. 1934-1946, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10. 10.18653/1/2022. findings-acl. 153. URL https: /ttps//t///aaaaooooooooooo www.gyyorrg//222 ff:22..fim Findingssaat
1537
Vinodkumar Prabhakaran, Ben Hutchinson, and Margaret Mitchell. Perturbation sensitivity anal- ysis to detect unintended model biases. In Kentaro Inui, Jing Jiang. Vincent Ng. and Xiao- jun Wan (eds.). Proceedings of the 2019 Conference on Empirical Methods in Natural Lan- guage Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLL) pp. 5740-5745, Hong Kong, China, November 2019. Association for Com- putational Linguistics. doi: (0.18653/v1/D19. 1578. URL
D19-1578/
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary Lipton. Learning to deceive with attention based explanations. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (eds.), Proceedings of the 58th Annual Meeting of the Association for Compula- tional Linguisties, pp. 4782-4793, Online, July 2020. Association for Computational Linguis- URL
tics. doi: 10.18653/v
acl main. 432/
Naveen Janaki Raman, Matco Espinosa Zarlenga, and Mateja Jamnik. Understanding inter- concept relationships in concept-based models. In Ruslan Salakhutdinov, Zico Kolter, Kather- ine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (eds.), Pro- ceedings of the 41st International Conference on Machine Learning, volume 235 of Proceed- ings of Machine Learning Research, pp. //proceedings.o . mll www...preesvvv23 20te335/raa2224... html
PMLR, 21-27 Jul 2024. URL https:
Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, and Xiang Ren. Tailoring self-rationalizees with multi-reward distillation. In The Twelfth International Conference on Learning Representations, 2024. URL https:
//peenevvew. httpe/vwwwwwwweeeeeeeeeeeeeeeeee wttt://orrmmm..-tee000......
Sukrut Rao, Moritz Böhle, Amin Parchami-Araghi, and Bernt Schiele. Studying how to efficiently and effectively guide models with explanations. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 1922-1933, 2023.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135-1144, 2016
Sarthak Roy, Ashish Harshvardhan. Animesh Mukherjee, and Punyajoy Saha. Probing LLMs for hate speech detection: strengths and vulnerabilities. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 61 1116661 6128. Singapore, December 2023. Association for Computational Linguistics. doi: 18.18653/v 1/22230 hto.://ww/0....nnnneeeemmme findings-emmlpp4007 URL httpp:://cclattheloogy. wrwp://3......00....../
Findings emnlp.dem 4077
Punyajoy Saha, Divyanshu Sheth, Kushal Kedia, Binny Mathew, and Animesh Mukherjee. Rationale-guided few-shot classification to detect abusive language. In ECAI, pp. 2041-2048, 2023. httpt:ttt/p.......te 3ttgg//000.333/A22200040
Nihar Sahoo, Himanshu Gupta, and Pushpak Bhattacharyya. Detecting unintended social bias in toxic language datasets. In Antske Fokkens and Vivek Srikumar (eds.), Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pp. 132-143, Abu Dhabi, United Arab Emirates (Hybrid), December 2022. Association for Computational Lin- guistics. doi: 10. 10.18653/11/2222.comm con 1om11111.077
URL https: / //acl anthe org./20.....
Maarten Sap. Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. The risk of racial bias in hate speech detection. In Anna Korhonen, David Traum, and Lluís Mårquez (eds.), Proceedings
18

---

Published as a conference paper at ICLR 2026
of the 57th Annual Meeting the Association for Computatiional Linguisties, pp. 1668-1677) Flo- rence, Italy, July 2019. Association for Computationnl Linguistics. doi: 1118555/1//19911111 URL hattpps//aclaatllooooooooo/////
Johannes Schäfer, UIrich Heid, and Roman Klinger. Hierarchical adversarial correction mitigate identity term bias in toxicity detection. In Orphée De Clereq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, and Shabnam Tafreshi (eds.), Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. pp. 35-51, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10. 1863/vvvv 2024. wassa- 1.4. URL htt.t::tt/saaaat /ttp:///caaaahaooooo logy. www.ggy.oooooooo owl.orgg////// 2024 wassa-1.47
Emily Sheng. Kai- Wei Chang, Premkumar Natarajan, and Nanyun Peng. The woman worked as a babysitter: On biases in language generation. In Kentaro Inui. Jing Jiang, Vincent Ng. and Xiaojun Wan (eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Lan- guage Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLPP,, pp. 3407-3412, Hong Kong, China, November 2019. Association for Com- putational Linguistics. doi: 1111188//1/DD1-111
URL attps://cclanthologg org/
D19- 119-1339/
Emily Sheng, Kai- Rei Chang, Prem Natarajan, and Nanyun Peng. Societal biases in language gen- eration: Progress and challenges. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguis- fics and the 11th International Joint Conference Natural Language Processing (Volume 1;
Long Papers), pp. 4275-4293, Online, August 2021. Association for Computational Linguis- URL
tics. doi: 10.18663/1/20221aalllomoo
acl-long. 3307
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In International conference on machine learning, pp. 3145- 3153. PMIR, 2017.
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Yoshua Bengio and Yann Le- Cun (eds.). 2nd International Conference Learning Representations, ICLR 2014, Banff. AB, Canada, April 14-16, 2014, Workshop Track Proceedings, 2014. URL http::/tp//ct pettp://arxiv.orr abs 11111311.262 6034
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM
Conference on AI, Ethics, and Society, AIES 20, pp. 180-186, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 97814548 7771450371100 100. doi: 10. 10.1145/3375627.3355533 1ttpp///..1111333333
URL
3375830
Gizem Sogancioglu, Heysem Kaya, and Albert Ali Salah. Using explainability for bias mitigation: A case study for fair recruitment assessment. In Proceedings of the 25th International Conference on Multimodal Interaction, ICMI '23, pp. 631 631-639, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400700552. doi: 10.1145/5577190.6644770
BRLhtts::
0//60...g/10..111///357777 361107
Alexander Stevens, Peter Deruyck, Ziboud Van Veldhoven, and Jan Vanthienen. Explainability and fairness in machine learning: Improve fair end-to-end lending for kiva. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1241-1248, 2020. doi: 10. 199/SSC14488. 2020.9308371.
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. Doina Precup and Yee Whye Teh (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 3319-3328. PMIR, 06- 11 Aug 2017.
URL httptss:/pprccediggs.oo mlr pressppeeps..................... htwpe////w////udarrrrerao
7
html
Yixin Wan and Kai-Wer Chang. White men lead, black women help? benchmarking and mitigating language agency social biases in LLMs. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova,
19

---

Published as a conference paper at ICLR 2026
and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of Association for Computational Linguistics (Volume 1: Long Papers), pp. 9082-9108, Vienna, Austria, July 2025. Association for Computational Linguisties. ISBN 979-8-89176-25111 2.5..a.c--on..44. htthttss::/ / antho . wwt. .al-:-22.cclll acw.ooog.....1onnn.ooo.oooooooo. 445/
doi: 10.18653/v1/
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Tal Linzen, Grzegorz Chrupat a, and Afra Alishahi (eds.), Proceedings of the 2018 EMNLP Workshop Black- boxNLP: Analyzing and Interpreting Neural Networks for NLP. pp. 353-355, Brussels, Belgium, November 2018. Association for Computational Linguistics. doi: N0.18653/11/11--555
URL
Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Sebastian Möller, and Vera Schmitt. Cross-refine:
Improving natural language explanation generation by learning in tandem. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (eds.). Proceedings of the / International Conference on Computational Linguis- tics, pp. 1 1150-1167, Abu Dhabi, UAE January 2025a. Association for Computational Linguis- tics. URL https: http://aaaaattt ho wtt.a/attt.oooooo.or///2........ coling main. 777
Xinru Wang and Ming Yin. Are explanations helpful? comparative study of the effects of explanations in ai-assisted decision- makiin... In Proceedings of the 26th International Confer- ence on Intelligent User Interfaces, IUI 221, pp. 318-328, New York, NY, USA, 2021. Associa- tion for Computing Machinery. ISBN 9781450380171. doi: 10.1145/3397481 .3450650. 1.4555000 URL 1ee11111433944
bttts:://do..org//
3450650
Yifan Wang and Vera Demberg. A parameter-efficien multi-objective approach to mitigate stereo- typical bias in language models. In Agnieszka Faleńska, Christine Basta, Marta Costa-jussä, Scraphina Goldfarb-Tarrant and Debora Nozza (eds.), Proceedings of the 5th Workshop on Gen- der Bias in Natural Language Processing (GeBNLP), pp. 1-19, Bangkok, Thailand, August 2024a. Association for Computational Linguistics. doi: 10.186622 (0.18653/v1/2) 1/2024..eb/..2.1....... URL wew.:221.....ee11.1..........
tttts::/aclaatthl.oo
Yifan Wang and Vera Demberg. RSA-control: A pragmatics pragmatics-gruunde lightweight controllable text generation framework. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Pro- ceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 5561-5582, Miami, Florida, USA, November 2024b. Association for Computational Linguss- tics. doi: 10. mannppmmiin.o 318/]
org/2024/
Yifan Wang. Sukrut Rao, Ji-Ung Lee, Mayank Jobanputra, and Vera Demberg. B-cos LM: Effi- ciently transforming pre-trained language models for improved explainability. Transactions on Machine Learning Research, 2025b. ISSN 2835-8856. URL http//tpp//pppeeeeeeee..
Gorum?idd=c18BUHHBB
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, and Slav Petrov. Measuring and reducing gendered correlations in pre-trained models. CoRR, abs/2010.06032, 2020. URL http::ttp httpt:ttsss......ooot/ wwwpe/iiii.oooa/aaaaa 2010.
An Yang. Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jian Yang, Jiaxi Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang. Qin Zhu, Rui Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin. Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report. CoRR, abs/2505 093888 2025a. doi: 10.48550/ARXIV25500 093888. URL htL.htts::/ //ttsp:://of orgg 10. 48550/ wrx. 2505. 09388
Xinyi Yang, Runzhe Zhan, Derek F. Wong, Shu Yang. Junchao Wu, and Lidia S. Chao. Rethinking prompt-based based debiasing in large language models. CoRR, wttps//5500022...... 2025b. doi: 10.48550/ ARXIV.2503.092. URL https: h/tbpps://dd/ .org 41550010.85550/// 2503. 09219
20

---

Published as a conference paper at ICLR 2026
Mengyu Ye, Tatsuki Kuribayashi, Goro Kobayashi, and Jun Suzuki. Can imput attributions plain inductive reasoning in in-context learning? In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilchvar (eds.). Findings of the Association for Computational Linguistics: ACI. 2025, pp. 21199-21225, Vienna, Austria, July 2025. Association for Compu- tational Linguisties. ISBN 8799-8889166-256-- doi: 10. 10.188533/1122 http://.......nnaaa....... findings-acl.1092. URL
www.aanttooooyy.or//222....... finss.iindigggg acl 1092/
Kayo Yin and Graham Neubig. Interpreting language models with contrastive explanations, In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 184-198, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. doi: 10.18653/v 1/2022. orq/
/ttp:////22.// 2022. emnlp-mane main. 14. URL https: htttpp::/accanttollooooo http://rrr...........p.eoep-e-.. emwlp-mmin........mmmam..m.mmmim 144
Xuemin Yu, Fahim Dalvi, Nadir Durrani, Marzia Nouri, and Hassan Sajjad. Latent concept-based explanation of NLP models. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp- 12435- 14435-124599 12459, Miami, Florida, USA, November 2024. Association for Computational Linguis- tics. doi: 10.18653/v 1/2034/1122222.o// 111p244.mmll-mmaa 692. URL ht httpps://aclaatcclorr/// 2tt.aaaanntooooooooo22222222 emnlp ma inain..... 692/
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai- Chang. Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Con- ference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume (Shorl Papers), pp. 15-20, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/1//N18-2000 URL
https:///claante ogy. www./oogg///// -2003
Fan Zhou, Yuzhou Mao, Liu Yu, Yi Yang, and Ting Zhong. Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Proceedings of the 6lst Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4227-4241, Toronto, Canada, July 2023, Association for Computational Linguistics. doi: (0.18653/v1/2023.acl) 232.
wwwp:ntwwwwoooooooo.oeooo wwwp:yy..rr.0002...... acl- 232/
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. Counterfactual data augmen- tation for mitigating gender stereotypes in languages with rich morphology. In Anna Korhonen, David Traum, and Lluís Mårquez (eds.), Proceedings of the 57th Annual Meeting of the Associ- ation for Computational Linguistics, pp. 1651-1661, Florence, Italy, July 2019, Association for URL
Computational Linguistics. doi: 10. 1/111663/1/1191111
Ptc/P19-11617
A FAIRNESS AND EXPLAINABILITY IN HATE SPEECH DETECTION
To better motivate our focus on fairness and explainability in the hate speech detection task, we provide additional background in this section. We begin by clarifying our definitions of hate speech and social bias, then review relevant work on faimess and explainability in hate speech detection. Finally, we explain why our study specifically focuses on input-based explanations.
Hate Speech We follow (Fortuna & Nunes 2018) in defining hate speech as a specific form of abusive or toxic language that targets and attacks protected or identifiable social groups Under this view, hate speech is a subset of abusive language. This definition consistent with widely adopted formulations in prior work on hate speech detection (e.g., Nobata (ebaa, et al., 2016 Davidson ctt al., 2017). Because our study focuses on fairness and, in particular, analyzes model behavior on examples involving specific social groups (race, gender, religion), this standard definition of hate speech aligns well with the scope and goals of our work. We still use the toxic vs. non-toxic labels following the terminology used in the Civil Comments (Borkan et al.) 2019) and Jigsaw (cjadams ct al.] 2019) datasets. Although these datasets include multiple subtypes of abusive content, they group them under the broader notion of toxicity.
21

---

Published as a conference paper at ICLR 2026
Social Bias Following the conceptualization of (Blodgett et al. 2020). we define social bias as the presence of stereotypical associations between social groups and certain attributes, as well as disparities in how these groups are treated as result. Such biases can lead to both representational harms (e.g., demeaning or misrepresenting targeted groups) and allocational harms (e.g., unfair distribution of opportunities amplify these harms, and their growing influence in everyday life, is essential to detect and mitigate social bias in these models.
resources). Given the potential for NLP systems reproduce or
Social Bias in Hate Speech Detection Social bias has been widely documented in hate speech detection systems. Dixon et al. 2018) showed that training data often contain uneven distributions of identity terms and stereotypical associations, which in turn propagates bias into downstream models. Subsequent studies revealed multiple dimensions of such disparities: [Sap et al. (2019)
demonstrated systematic dialectal prejudice against African-American English (AAE), while Park iet al. (2018) reported significant performance gaps across gendered identities. Garg et al. (2019)
further found that models frequently assign different toxicity labels to otherwise identical content when only the referenced social group is varied. [Sahoo et al. (2022) expanded the scope of this line of study by curating the ToxicBias dataset and examining bias across a broader set of social categories. More recently, Roy et al. (2023) found that LLMS exhibit similar bias in hate speech detection. Together, these studies underscore the persistence and multifaceted nature of social bias in hate speech detection.
To address such biases, a rich line of work has proposed mitigation techniques at different stages of the modeling pipeline. Pre-processing methods include debiasing word embeddings to reduce spurious associations between identity terms and toxicity (Park et al.) 2018), re-sampling or re- weighting examples to obtain more balanced label distributions across identity groups (Dixon al.,
2018), and counterfactual data augmentation (Park et al. 2018 Garg et al. 2019). In-processing approaches mostly modify the training objective, for instance by adding faimess-aware regularizers that penalize correlations between identity terms and toxic predictions (Garg et al. 2019 Davani al., 22200] Attanasio et al.] 2022 Schäfer et al. (024). Post-processing methods adjust model outputs without retraining (Wang & Demberg] (024b):: threshold adjustment per group has been used trade off subgroup false positive and false negative rates and reduce disparities (Dixon al., 2018), while Mamta et al. (2024) identify neurons associated with biased behavior and prune or edit them to improve fairness.
Despite substantial progress on identifying and mitigating social bias in hate speech detection, rela- tively little work has systematically explored whether model explanations can be leveraged to detect or reduce such biases.
Explainability in Hate Speech Detection In parallel, there is a growing line of work on input- based explanations for hate speech detection. HateXplain (Mathew al., 2021) introduces a bench- mark with numan-annotade
rationales and shows that models trained with rationale supervision improve both interpretability and reduce unintended bias towards target communities. Building on this, Kim et al. (2022) and [Saha et al. (2023) train models to jointly predict human rationales and toxicity labels, leading to more robust and explainable hate speech detection systems. More recent work further leverages LLM-ggnerated rationales to supervise hate speech classifiers, achieving im- proved performance and interpretability (Nirmal et al. 2024).
However, while existing efforts focus primarily on improving hate speech detection performance, relatively little work examines whether and how input-based explanations can be systematically leveraged to improve fairness in hate speech detection models. Since faimess and explainability have both been extensively studied in this task, hate speech detection serves as an ideal setting for a thorough empirical examination of how these two dimensions interact in NLP models.
Input-Basee Based Explanations We focus on input-based explanations because they offer the most direct view into which parts the input influence a model's prediction (Wang & Yin 202 and they have long been regarded central tools for fairness auditing in ML (Balkir et al. 2022 Deck et al. 2024). Their methodological diversity also makes them an ideal testbed for our study, en- abling comprehensive examination of whether and how explanations can improve fairness (Lyu ct al.) 2024). In addition, both automated and human- metrics for evaluating explanation properties (e.g., faithfulness, interpretability) are well established YDeng al. 2020 Jacovi &
22

---

Published as a conference paper at ICLR 2026
Goldberg 2020 Lage et al. 2019). This allows analyze how these properties relate to
planation method's (in)effectiveness in faimess-related tasks. Finally, input-based explanations are often mandated by laws, such as the EU Artificial Intelligence Act, making it practically important to understand how their use interacts with fairness considerations.
B DETAILS ON EXPERIMENTAL SETUPS
Table 3: Splits for the Civil Comments and Jigsaw datasets used in this work. The instances are sampled randomly from the original dataset.
Split
Civil Comments
Jigsaw gender
race
gender
religion
race
religion
Test
2,000
2,000
1,000
400
800
200
Training
8,000
8,000
6,300
8,000
8,000
6,300
Datasets and Models Table shows the dataset statistics for the Civil Comments and Jigsaw datasets, respectively. When constructing subsets for each social group, include only examples that contain words from the corresponding group's vocabulary and exclude those containing words from other groups. Additionally, remove examples that include insulting references social groups.
For encoder-only models, either train separate models for each bias type single model the combined set of all bias types. The latter are marked as (all) in the results. Models are trained for epochs with batch size of learning rate of x 10-5 and 10% warm-up steps. We implement debiasing methods of encoder-only models ourselves. Debiased models are trained on the same number of examples for comparability, therefore do sample more examples from external datasets for causal debias. All experiments are carried out using maximum of 4x NVIDIA H100 GPUs.
We use the base version of BERT and RoBERTa models for our encoder-only experiments, and for decoder-only experiments, we adopt the instruct versions of the models from HuggingFace.
Prompts We employ the following prompt for zero-shot and few-shot prompting with decoder- only models.
Determine whether the following sentence contains any toxic content. Toxicity includes insults, threats, hate speech, or any language that is likely to offend or harm others.
Task:
([EXAMPLES])
(Now, please analyze the following sentence and provide your answer. Answer only Yes No.)
Sentence: [TEST EXAMPLE]
Question: Does this sentence contain toxicity?
Answer (Yes or No)
We use one toxic and one non-toxic example for each social group associated with a bias type to construct the corresponding few-shot prompt. For faimess imagination, prepend the task instruction with:
Imagine a world with no bias regarding [BIAS TYPE]
https//hhgginngaaccc./aatttsssss htt.://gggng.atooo/ddooco/////ee comments helm https//hwugnnnhaee..oooaattea/ep unintended bias
23

---

Published as a conference paper at ICLR 2026
For fairnsss instruction, add:
In the following, do not rely on stereotypes or biases regarding [BIAS TYPE] make predictions.
Input-Based Explanation Methods We implement Attention, Attention Rollout, Attention Flow, and Progressive Inference ourselves, and use the official codebase from the DecompX paper for its implementation. For other explanation methods, generate them using the Captum APP Single- pass Progressive Inference used for simplicity.
Explanation-Basees Bias Mitigation We reimplement all explanation methods used in RQ3 to enable gradient tracking during training and ensure consistency with the explanations provided by the Captum API. For Occlusion, we simplify computation by penalizing the probability difference between the original input and the input with all sensitive tokens masked.
LLM decision and LLM attribution To elicit bias judgments from LLMs, we append the fol- lowing instructions to their predictions.
LLM decision:
not re-answer the task. Instead, reflect only on your previous answer.
Was your previous answer influenced by stereotypes biases related to (BIAS TYPE]?
Question:
Answer (Yes or No)
LLM attribution:
Now select exactly [NUM_TOKENS] words from the input sentence that most influenced your decision. List the words separated by commas. Please begin directly with the selected word.
Selected words:
C EXPLANATION EFFICIENCY
Table reports the time and GPU memory costs for each explanation method. Most post-hoc ex- planation methods are lightweight when applied to BERT, whereas IntGrad, Occlusion, and Ker- nelSHAP require substantially more time and computational resources when generating explana- tions for LLMs.
D MODEL PERFORMANCE AND FAIRNESS RESULTS
Tables 5) and show the task performance and fairness scores for the default/zero-sho and debiased models on the Civil Comments and Jigsaw datasets respectively. To better identify the differences between different debiasing methods, conduct analysis based on how often debiasing method successfully reduces the average individual unfairness (Avg,,) and maintains the task performance (Accuracy) of the default/zero-sho model.
Encoder-only models Analyzing the results with respect to the dataset, we find that the models are able better preserve their original accuracy on the Civil Comments dataset (48.61% of the cases) compared to the Jigsaw dataset (40.28% of the cases). In contrast, mitigating bias seems substantially easier on the Jigsaw dataset (in 63.88% of the cases) than on the Civil Comments (only 50% of the cases). On closer inspection, find that this skew comes from religion bias
hhtpss//captuu.a//api
24

---

Published as a conference paper at ICLR 2026
Table 4: Computational costs per example for generating explanations across 200 instances on BERT and Qwen3-4B. Results are computed on the race subset of the Civil Comments dataset using a batch size of and are averaged over three runs. All methods are run on a single 80-GB H100 GPU, except Integrated Gradients, which uses two H100 GPUs with gradient checkpointing to reduce memory usage. Because explanation methods within the same family incur similar computational costs, we report each family only once.
BERT Time (slexample)
Qwen3-4B Time (s/exumple)
Method
Memory (GB)
Method
Memory (GB)
Attention
0.027
0.529
Attention
0.112
16.598
Grad
0.026
0.603
Grad
0.237
19.631
IxG
0.025
0.603
IxG
0.236
19.631
IntGrad
IntGrad
1.784
DiepLift
0.027
0.748
Deeplift
0.323
23.530
Occlusion
0,330
0.508
Occlusion
4.204
15.639
KeracISHAP
KemelSHAP
1.374
20.013
DecompX
0.074
7.222
Proglafer
0.068
15.810
Table 5: Task performance and fairness of default and debiased models on the Civil Comments dataset. Results are provided for race/genderrretigionn biases. Green (red) indicates the results better (worse) than the default/zero-shot models. All indicates the model is trained on data containing all bias types.
Model
Method
Accuracy (1)
(U)
Dispgr (3)
Disp
Avgu(-)
Default
78.38/88.0588 05/85.9 93
30/18.7
0.5000.03/5.77 77
10.04/1 1, 1,98/30.90
3.17/0.66/1.2
Group balance
79.25/87 25/86.83
11102.80013.5
0,25/1.73/11.55
10.46/5. 38/30.11
3,79/)
Group-elass halance
25.00/87.02888.
1.802275/14.72
2.42/1.99/33
10.6377 21387.26/33.11 14
4.43/0
BERT
CDA
76,83/86. 70/34,83
13//3.60/14.11
5.88/2 01//2.005567
18.45/7.5700 57/24.12 12
0.500 5000.9
Drapout
0..53/8.20085.00
2.25/2. 10/15.77
0.78/1.46/5.0
10.22/350/27.16
Attention ontropy
79.15/87.6748 67784.993
2.60/2.05/15.07
1.9990.1004.90
11.7177 1 1/266.52
2,95/0.67/1 .5
Causal debias
18.80/86.17786.40
D.0W2.65/16.40
3.9000.46/8.8 82
7.98/10.6733046
3.83/0
Default
78.30/88, 20/87.43
2.01M3. 20/1347
0.02/1.11/6.26
8.44/8.58/33.5 53
3.99/0.96/ 96/1.76
Group halance
79:05/88.8588 85/87.47
3. 3.50/2.8 5/0/2.0/1330 80/13.67
1.72/0. 110001166.00 /6.92
8.83/1 .08/23.91
4. 4,13/1 17/2.
BERT (all)
CDA
Group claup-classue balance
78.17/88.22 2.886.5566.90
78.08/87. 70/86883
199/1.70/14 70/1
2.68/2 20//2/00114 4.33
1.35/0,51/9 .2
6.38/1 LO5/4. 70
20. 35.3566, 92/30.23
9.33/4
4,8300 931199311.3
O. 0.600 4600.7
Dropout
78.08/87, 60887.67
1.45//,10/13.44
0,30/1 1.005.555 55
39/3339//3.11
3. 3.60 89/1.59
Attention entropy
78.35667.9000 990887777
10/02 30/1
1,2800 1066.5
5.92%%001356
4. 4,9%/0 9612.
Causal debias
79.40/88.5/877777 7,
2.2002660/12.66
2.510070006.70
3,54/0 12
Default
0..50/8.33/85.2
2. 2,8022.00 NON2.05/1..0
2.84/1 666/6.99
15.46/2.708 016/2.78/31.66 64
2.56/0 60V1.55
Group bulance
78.25/58. 50/87.03
2. 2.0122 6.99
2.101.27/1 1.36
38544.57/20 48
3. 68/1..68/1.19
Group-clase halance
78.57/84.50083
I 1,6/2, 30188.80 80
3.310.76/3.88
12.91/5 82/ 88
3 22/00420.8 420.87
RoBERTa
CDA
06.75487.5885.20
26/1255/14.00
6.3770.31/4410
15.91/5
0,82/0
Dropout
78.33/98.922 9/28869999//6733
2. 15/11 .55/ 4.53
2 2.422..88888 86
11. 11/3 912.99922000 005
4,08/0 562
Atteation entropy
1.33358.42/8661
L7551 75/15.73
2,8900 23/9.23
10.91/5 60/.660/4666 2.66
3,82/0 6911.75
Causal debins
0.83487.52/66.0
2.65/2.45/15.00
1,48/0 85/10.66
11,3466.5 1/30.14
4.0600 56/1.34
Default
78.88/88. 70/978.00/8799 7.9
2.95/2400/1.880
2,24/0,58 58/9.55
13.55/7. 19/33.47
4, 14/00 95/2.35
Group balance
79.30/88,C0 677788665777799
19992.0014.77
1,27/0 17/12.30
11.03/7.7446 L
5.02/1 00..06122600
RoBERTa
(all)
Group class balunce
CDA
72.75/88.25/8660 25/38.85//86.9 90
19.47/99.15/87.93
2. 2.5002.00/1 3..03.80
7.7011 10/12.73
4.43/0.24/5.0
5.93/1.2566.3
13.65/3.24P 25/3224//2599 90
18. 80.8033, 11/22.62
1,1 L.30055//1 18
7.170.7551.5 .58
Drapout
78.88/58.400 40087.70
2.75/3 00010000144.800
2.3163336.666 66
12.46/7.344 /4/3773/433339
4,26/0 99/2,
Attention entropy
78.80/98.72/774
2. 10/2, 15/13.53
2. 2.64/1 33/7.55
11. 11.31/4, 18/28.68
4, 46V1 09/2.57
Causal debias
79.27789.78/57.88
3.35/1.25/15.00
3,24/0.5111 51/1
16.00/3.05/37.57
3.56/0.7442,70
Zero-sbot
63.78/74.6271 62271..7
L.45/2.3//4.67
11.03/3.5/336
10.54/1.03/2.99 95
1/ 94/3.83
Lluma3,2- 3.333 II-ssstruct
Few-shot
67.80/79.80088 10
L6001170/18 20
2. 2.49/0.0448 73
6.73/6 7./3600//10.77
1,39/2.055 00511 990
Faimoss imagination
11.95/75.2273..3
9000885//1.87
8,70/3 6132..5
9.4416.79/5 79667995.99
2.65/3 5813.50
Faimess instruction
65.90/76.95/78.07
2. 1.601170/21.53
1.89003997700
3. 3.79/6.35/4.24
1.35/1.13// 13/11.1
Zero-shot
59/58/79.75/77.50
D.60N0.00/17.40
7.13/1 4022007
13.25/3.71/5. 17
2.55/2 (55/2.41//3.5 41/3.32
Qwen3-4B
Few-shot
00.15/80/73/79.55
95.
19/9
3.13 34/3.6 76
Faimoss imagination
71 71.23/80.40/800.8
0.85/1 .0V18.27
4.03/2.1 11/10.51
11 1.6229.11444 21
2.98/3. 16/2,22
Faimess instraction
70:40/79:77/80.47
0.60/1. 35/1333
4,30M) 39/4.67
11/11/522//
2,02/11 831171
Zero-sbot
59.27/69 23/66.00
8.18/0,07M2.00 1.2580.1//6.80
4.65/0.80/3.02
3.27/3 40M4.4
Qwen3-8R
Fow-shot
Faimess imagination
66.97777.85 330/7744777
62.10/72.9/699.9
0.05/0.00223.2 22
L6600.55/1187
7.80/2. 62/32.27
6. 14/2273//9..5
7.95/T 64/2.34
42295442/9.43
4 4.23/4 58/5 96
2.54/2.0K 0822.58
Faimess instruction
10.50/75.15/73.0
0.900110021..0
8.03/1.76288 76/28.60
8.79)4.5977.5
15,
in the Jigsaw dataset which is improved in 95.83% of the cases after debiasing, followed by race
bias (50%) and gender bias (45.83%). In the Civil Comments dataset, find that gender bias is mitigated best (improvement in 62.5% of the cases), followed by religion bias (54.17) and race bias (33.33%)
25

---

Published as a conference paper at ICLR 2026
Table 6: Task performance and fairness results of default and debiased models on the Jigsaw dataset.
Results are provided for race/gender/rcligion biases. Green (red) indicates the results are better (worse) than the default/zero-shn models. All indicates the model trained on data containing all bias types.
Model
Method
Accuracy (1)
(4)
Disp:. (3)
Disp,,(() (4)
A-g
Default
85.50/93.000 0009000000000
0,50/2.25/600
0.64/2.34/5.22 22
0.70/3288) .4
2.02/0.36/1.3
Group bulance
84.88/92.788 7///9997//89677
2.75/1.00110.6
1.28/0.82/3.90
7.77/4.56/35 06/3566638.2
1.900.36/066
Group-class balance
84.38/92.81/9088
0.25/0.62/6.35
1.580.15//199
8.03/9.64411.55 557
0,97/0.65X0.3 34
BERT
CDA
85.25/91.81/98
4.003.63110.00
4.12/3.44/510
29977738/37.39
0.39/0.2500 25/0.45
Dropout
85,62/92. 69/89.88888
1,25//337/9.67 66
0.31/3,03/5.66
6.51/88 37
2.75/0,36/1.00 .00
Attention entropy
(00/92..0//9.88
0.00/3.1/99 33
0.62/3.03/4.29
1.72/6 216..00288000 06
2,93/0 5000.9
Causal debias
85.50/93338/89..8 83
4.00/0.55/138
1,28/0 28/33.28/3.55
13/75/1.777/1.1
3.160/43/11
Default
85.62/33. 19/90.33
1.28/112//.33
51/4/5
12.69A) 36/21.76
13300.3/1..0 18
Group balance
13.98/93.19/0017
1.75/1,12/.67
1.56/11044.66
3.10/3.23/26.
2,81/0.400,70 76
BERT (all)
CDA
Group-class balance
84.88/92.94/00.00
85.62/92. 19/90.00
1.250.87/10000
1-25/188/7.00 .00
2. 2/36/178//00
112200,41/2.00
0.377) 49/58
11774441/38
0.69/0.29K0.0 46
1.29/0.28K0.4 47
Drapout
86.50/93.4400 1...44499100 1.00
3.00
1.25//.1005.6
10.2416, 10/13.16
1.910033/1.2
Attention entropy
85.25/93.75/91.5
0.50X2.7508.0 00
0.65/2.62/5.19
0.57/5.54/345
2,57/0.41/1.00
Causal dehias
84.5093.44/99.50
1.0001.3899.00 00
2.22/1.38/4.22 2.34/227
4,35/3. 04/3..8/24.88 10
1.400 100
Default
$4.50/93.00/9..33
33
2.87/3 .22
6.54/8 34/8731147.44
1.55/0.300..3
Group balance
85 05.500/2.310 1/889.88
2. 6250066211 1 1:33
O. 0.940027/1. SS
9.1 1///41/38 00
2. 2,44/0 260 46
Group-class ballance
85.0092.50099 577
1.0015005 33
L.590.26/2.0 01
115/14.87//4..
1.55/0 53X0.62 62
RoBEKTa
CDA
85.12/93. 19/89,93 33
0.75/1 8808 62
4. 1221 L0VE 90
12.64 12.64/11 1. 3/25
3.360 2310
Dropout
83.88/9369999
1.75K0887 79
L,2900 83/2.97
3.10/3 28/26.86
2.71/0.23K1.87
Attention entropy
0.50V1.750667
2.23/2.06/1. 10
6.55/0.62/22.77 78
19/002240.81
Causal debias
06.25/92.19/89.55
2.003.37/10.00
2,23/2.33/184
0,60/14.77443.4
2.09/0.390066
Default
85.50/93.75/0 1,50
0,50/1,75/2 O00
R.111.51/S 56
3 74/31 14
2,52/0 00/.3511..5 55
Group balance
85.38/93.66 619996662116677
1.75/3.25/9.5
0.01/2.4774.
9,01/1 1...0000222 29
2.7660 3000,0
RoDERTa
Group-elass balance
86,38/92. 56/90.17
2.25/1 88/10.67
33/2.58
9.0588 62/6626444 35
4,75/0.230.5
(all)
CDA
85.25/92.56/99 56/90.67
1.000.6277 62/7 .7
1.590.13/1.8 80
11.53/7 19/31 228
0.52/0.23K0.
Dropont
86.00/93 0000009 17
2. 75/4
1.27/11 51/4.19
17. 17.516,21/22.777
101/0.3310,7 79
Attention entropy
86.75/93.5099 1.5593350009.50
0,50/2.50/7.0 .00
D. 1966/2
..44/.055/24.59
3. 3.400033// 1000.38/111 19
Causal debias
85.38/93.25991.00
0.25/3.50/10.00
0.01/2 ..01/2.6//5.41
1.88/13699/34.1 14
2.55/0 40050.0000.880
Zero-shot
54.00/70.5065.11
8.5001.00/55.6
10.91/1.53/3 87
0.204456/8.33
2,39/3 00040044
-lamm3,2-38--nsittt
Few-shot
73.12/88. 62786.8
7,25/0 50/3 67
13.01A0. 164.83
15.089 00/0000/333 17
1,33 68/2.00
Fuimess imagination
77.25/73.56/66.8
5.001 62226633
6.47/1 6330.03
2 2.8633773 734.92
Paimess imagination
52.75/73.56666 56/66688
5.00/1 162266633
6,47/1 63130.03
026/1.74/11 48
2.8663.7333 92
Fairncss instruction
77.00789.00857
2.0000,5/11.66
2.87/0.84/3.97
2.19/3.33/6636
1.39/0.97/1.88
Zero-sbot
66.75/77 25/77.33
3.505. 73/1633
4,21/3.78/17 78/17.40
0004005/5.8 89
3.05/2.311 31/3.67
Qwen3-4B
Few-shat
57.88/68.06/77
8.75/2.12/9.8 12/9.33
11.52/1 80/10.88 86
749/5.99/945
3.60/4.3 18
Faimnsss inagination
73.75/62.88/866.2
3.0011 00/10.0
5.12/0 89/5,79
5,39/0 82/24.13
Faimess instruction
78.00/89. 50089.33
3.0000.50/9.3 33
14/40/2/3.11
2.04/5 23/26.7/
1.95/1 .61
Zero-shot
48.12/59.588 50/59
7.25/0.00/13.00
168/0.37/18.59
1.31/4.9/6.33 30
3.31/3 4.311/.44//5552
Qwen3-8B
Few-shot
53.75467. 19/77.17
5.50n 1228.67
6.18/1 53/10.51
3.41/1. 95/4.88
4.50/5 0445
Faimasss imagination
51.6267.5000 013667550061888 1.3
4.25/0.75/9.6
5,24/0.3611 1.23
1,0/3349/6644
2.51/2.0022.55
Faimess instruction
60.25/71.19667.55
8.50/1887/1..00
10.51/223/14.22
0.93/1.902.110
2.46/3.13/560
With respect to the debiasing method, we find that CDA performs best in terms of debiasing, as it reduces Avgi across all bias types, datasets, and models. The second best performing method is group-class balance which manages to reduce Avg,, in 58.33% of the cases on the Civil Comments dataset and in 75% cases on the Jigsaw dataset. For the other methods, the results are mixed as we again observe dataset-specific differences. For example, we find that Attention entropy performs well on the Jigsaw dataset (50%) but performs worst on the Civil Comments dataset (16.67%). These differences become even more pronounced when looking at different bias types. For instance, causal debiasing improves Avg., for religion bias across all models on the Jigsaw dataset but at the same time, does not improve single model in terms of Avgi, for gender bias in the same dataset. Interestingly, we find an inverse trend on the Civil Comments dataset; i.e., causal debiasing succeeds on all models for gender bias, but only for one model for religion bias. These findings highlight the importance of considering a diverse set of datasets for evaluating debiasing methods, as results on a single dataset can be misleading.
Decoder-only models We find that the debiasing methods (fairness imagination and fairness in- struction) for the decoder-only models consistently improve the task performance across all bias types and datasets. Contrary to this, we see increases in average individual unfairness of the fair- ness imagination approach for race and gender bias on Llama3.2 3.3BBBBBBBBBBB 2-3B-Instruct and Qwen3-4B across both datasets. Only for religion, fairness imagination leads to consistent decrease of the individual unfairness across models. For fairness instruction, we observe a consistent improvement across all three bias types and both datasets, showing the clear superiority of the approach. The consistency of
26

---

Published as a conference paper at ICLR 2026
the results is especially surprising when considering that both decoder-only models instruction- tuned and aligned with human values, and that Chen et al. (2025) identify a bias amplification effect from instruction tuning. We conclude that fairness instruction good baseline to evaluate other debiasing methods for decoder-only models.
E BIAS DETECTION RESULTS
Fairness correlation We present the full fairness correlation results of encoder- and decoder-oni. models with different debiasing methods on Civil Comments and Jigsaw in Figures7 9 T0 Consistent with findings presented in the main text, Occlusion- and L.--based explanation methods achieve strong fairness correlations across different setups.
Comparing different debiasing methods, we find that low correlation scores primarily occur when individual unfairness less pronounced, such as CDA models. these cases, the models them- selves produce fewer biased predictions, making the detection of bias through explanations less critical. The lower correlations therefore do not substantially undermine the role explanations in bias identification.
F MODEL SELECTION RESULTS
Explanation-Based Metries and Fair Model Selection Results We evaluate several explanation- based metrics for selecting fair models with respect to different fairness criteria:
Average absolute sensitive token reliance: used to predict average individual unfairness, under the assumption that higher reliance on sensitive tokens implies greater sensitivity to group substitutions.
Group differences in average absolute sensitive token reliance: used to predict dispari- ties accuracy, assuming that stronger reliance on sensitive features increases the risk of incorrect predictions.
Group differences in average absolute sensitive token reliance for positive/negative predictions: used to predict disparities in false positive and false negative rates, respec- tively.
Among these, only average absolute sensitive token reliance exhibits rank correlations above dom chance with its target fairness metric (individual unfairness). The correlations for other metrics remain chance level. Figures tently match baseline rank correlation results.
12 13 14 demonstrate that no explanation methods consis-
Figures 15
T6 7 18 further reveal that explanation methods are not able to robustly select the fairest models. These findings underline the unreliability of explanation-based model selection.
G BIAS MITIGATION RESULTS
The findings are in line with conclusions from the main paper, that explanation-basee debiasing can effectively reduce model biases across different fairness metrics, bias types, models, and datasets. In addition, the accuracy- fairness harmonic mean results shown Figures 23 24 25 26 demonstrate that explanation based debiasing achieves comparable superior balance between fairness and task performance than de- fault models and traditional debiasing approaches.
The complete bias mitigation results are presented in Figures 20 21 22
We additionally report the results of Integrated Gradients for bias mitigation in Table Similar to debiasing achieves substantial bias reduction and main- tains good balance between faimesss and task performance in Disp and Avg
other explanation methods,
27

---

Published as a conference paper at ICLR 2026
BERT
Delaatt
Group belance
Groupclass balance
EDA
Allent
Artn rolout
Altn feww
Gred mean
Grod 12
IGG
Race
Gender
Rei glon
Raice
Gender
Rel glon
Race
Gender
Reig
Rade
Gender
Re Igian
IG L2
InfGred
IntGrad
Drogout
Actention europy
Causal dabias
Kemelzesyy
Decompo
Roco
Gender
Religion
Racce
Gender
Rel gion
Race
Gender
Re on
BERT (All)
balance
Group balance
IxG mean
Gender
gion
Gender
gm
Racm
Reviig
Race
Glander
Ra lginion
Intorad moon
lsG
IntGred L2
Deeplit mean
Deoplitt L2
Docusion
Doclusion
KemelSHMP
REBER
Dillattt
Group balance
Group-clas balaacee
CDA
Artent
Aztn rollout
Atth flww
Gender
Gender
Gender
DospLitt moan
Dropout
Attention entrosy
Csusal deblas
Deeplit
Occusion
Doc usion abs
KamalsitMP
Decompx
Gander
Rack
Gender
Clender
Religg
RoBERTa talo
Default
Group balance
Group-llass balance
CDA
Allent
Arin rollout
Adin flww
Grad mean
Grad
laG meen
Rac-
Gunder
Pall gion
Rack
Gender
Rell gion
Race
Gender
Race
Gender
Ro lgion
HnlGred
IxS L2
HEGraad
Drosout
entropy
Causill
Dooplitt L2
Docompa
Racee
Gender
Religion
Race
Gender
Rel glon
Race
Gendder
om
signtficant
but one signtficant
Figure 7: Fairness correlation results Civil Comments for each explanation method across encoder-only models and bias types. Higher values indicate that the method is more effective and reliable in detecting biased predictions inference time. All indicates the model is trained data containing all bias types.
28

---

Published as a conference paper at ICLR 2026
1e1332233----ee Insttuutt
imaginatin
Faim instruction
Raice
Gender
Relgton
Race
Gender
Re
Race
Gender
Relgion
Race
Gender
Relgion
Qwen3-48
Zero shot
Few Few-sott
Falmess imagination
Raliness Instruction
mekes
Grad
IxG moan
IxG L2
IntGred mive
Intorad 12
Desplift moan
Deeplit 12
Oocusion
Raice
Gender
Rdigton
Race
Gonder
Re on
Roce
Gonder
Religion
Race
Gender
Rolgion
Occiusion
KamalSHuP
Owen3-80
Zoro shot
Few shot
Falmess Imagination
Ralmnss Instruction
1000
Gender
Beligton
Race
Gender
Race
Geerdrr
Race
Gender
signtficant but signifiant
Figure 8: Fairness correlation results Civil Comments for each explanation method across decoder-only models and bias types. Higher values indicate that the method is more effective and reliable in detecting biased predictions at inference time.
Table 7: Results of mitigating on Civil Comments. For consistency with accuracy, fairness results are reported as 100 (Dispace DisPipr Dispine Avg.). so that higher values indicate better debiasing perfor- mance. Each column corresponds to models selected by maximizing the fairness- fairness-balanced metric with respect to the indicated bias metric. H-Mean indicates harmonic mean between fairness and accuracy. Green (red) indicates the results are better (worse) than the default models.
bias in BERT models using Intgrad explanations
Method
Disp Fairness
Dispar Fairesss
Disp Faimess
Ave.. Faimess
Acc
H-Mean
H-Mean
Acc
H-Mean
Acc
H-Mean
IntGrad I.1
78.55
96.66
86.67
78.55
96.66
86.67
77.98
97.86
86.8
78.37
97.02
86.7
IntGrad L.2
77.85
96.58
86.21
77.71
96.33
86.02
77.85
96.58
86.21
78.09
97.1
86.56
Default
98.37
87.61
84.62
78.97
96.36
H GENERALIZATION OF FINDINGS
To demonstrate the generalizability of our findings, we present results under additional setups that vary in task, model alignment type, and sensitive token vocabulary. We observe similar results across these setups, suggesting that our findings generalize well beyond the main study conditions.
Task We evaluated explanation- bias detection (RQ1) on an additional task, namely senti- ment analysis, using the Twitter Sentiment datase(") Specifically, we selected 1000 gender-related examples (500 referencing males and 500 referencing females) and ran explanation based bias de- tection them. In Figure 27 report the results on Llama3.2-3B 3-3nnn-rtutttt
Instruct and Qwen3-4B models.
In Figure 27 we observe patterns in the sentiment analysis task that are similar to those our main study: certain explanation methods (e.g., occlusion-based and L2- approaches) still
http://mnugggngieeccoodosssssshn 23/witter sentiment preprocessed
29

---

Published as a conference paper at ICLR 2026
BERT
Deliatt
Group belance
Group-class balance
EDA
Allent
Artn rolout
Altn feww
Gred mean
Grod 12
IGG mean
Race
Gender
Rel glon
Raiee
Gender
Rel glon
Race
Gender
Reig
Race
Gender
Re Igian
InG L2
InfGred
Drogout
Actention enropy
Causal dabias
Kerelzesyy
Decompo
Raco
der
glon
Racee
Gender
Rel gion
Race
Gender
Re on
BERT (All)
Default
Group
Group class balance
IxG mean
Giender
gion
gnon
Glender
Ha Maiiiion
Intorad moon
lsG
IntGred L2
Deeplit mean
Deoplitt L2
Docusionn
Doclusion
KemelSHMP
BERTO
Dillautt
Group balance
Group lasa balance
CDA
Artention
Aztn rollout
Acth flww
Gender
Gender
DospLitt moan
Dropout
Actention entrosy
Csusal deblas
Deeplit
Occusionn
Doc usion abs
KamalsitMP
Decompx
Gunder
Race
Gander
Clender
Relg
RoBERT talo
Defauft
Group balance
Group-llass balance
Atlent
Arin rolsou
Adin flww
Grad mean
Grad
TaC meen
Race
Glander
Rall gion
Race
Gender
Rell gion
Race
Gender
Ralligg
Race
Gender
Ra lgion
HnlGied
BGS L2
HEGraad
Drosout
entropy
Causill
Dooplitt L2
Docompa
Race
Gender
Religion
Race
Gender
Relgion
Race
Gender
Re-
sightfiann
but significant
Figure 9: Fairness correlation results Jigsaw for each explanation method across encoder-only models and bias types. Higher values indicate that the method is more effective and reliable in detecting biased predictions inference time. All indicates the model is trained on data containing all bias types.
achieve high fairness correlation scores. This suggests that our findings could extend beyond the hate speech detection task.
30

---

Published as a conference paper at ICLR 2026
Lssm3.2-39 In-instucc
Falmess imaginatin
instruction
Paice
Gender
Relgton
Race
Gender
Re
Race
Gender
Relgion
Race
Gender
Relgion
Qwen3-48
Zero shot
Few swwshot
Falmess imagination
Paliness Instruction
makes
Grad
IxG moan
IxG L2
IntGGeed move
Intorad 12
Desplift moan
Deeplit 12
Oocusion
Raice
Gender
Reigton
Race
Gonder
Re delgioo
Race
Gonder
Religion
Race
Gender
Roligion
Occiusion
KamalSHuP
Owen3-80
Zoro shot
Few shot
Falmess Imagination
Ralmnss Instruction
Gender
Belitonn
Race
Gendder
Race
Gender
Race
signtficnnt but signifiant
Figure 10: Fairness correlation results Jigsaw for each explanation method across decoder-only models and bias types. Higher values indicate that the method is more effective and reliable in detecting biased predictions inference time.
Model Alignment Type We extended our experiments to additional LLMs with different align- ment methods. Specifically, we evaluated explanation-based bias detection (RQ1) on two differently aligned LLMs: Llama3.2-31 3-BBBB (pre-trained only. non-instruct, used with few-shot prompting) and Owen2.5-3B-Instrus (instruction-tunn
only). Neither model is aligned to human values, which differs from the models used in our main study. The results are computed for race bias on Civil Comments and shown in Figure 28
We observe that certain explanation methods, such as Occlusion, consistently achieve high fairness correlations. This suggests that findings generalize across LLMs with different alignment set- tings.
Sensitive Token Vocabulary In practice, is often unrealistic exhaustively enumerate all vo- cabulary items that may encode sensitive attributes. To assess the applicability of our findings under such conditions, we analyze how varying the coverage of sensitive tokens affects bias detection and mitigation. Specifically, we focus on gender bias and use small subset of gendered pronouns ("he/his/him" and "she/her") as sensitive tokens, while computing fairness metrics with the full rendte- vocabulary (222 words per gender). This setup simulates real-world scenarios where the sensitive vocabulary cannot be fully enumerated.
As shown in Figures 2 and 30 reduced vocabulary coverage has minimal impact explanation- based bias detection and mitigation performance. This result is reassuring, suggesting that explana- tion methods remain effective in more complex, realistic settings where exhaustive sensitive token coverage is infeasible.
I FAITHFULNESS AS AN INDICATOR OF BIAS DETECTION ABILITY
What factors influence the reliability of explanations in detecting bias? this section, we exam- ine the relationship between explanation faithfulness and their ability to identify bias, reflected by
31

---

Published as a conference paper at ICLR 2026
BERT
Correation
0.75
0.2
-325
Seligion
BERT (all)
Cormatiinn
faimess (baseing]
0.90
Attontion
Attn rollout
flow
Grad 12
IxG mean
Race
der
Religion
L2
RoBERTO
IntGrad L2
Rant Cormattio
0.75
Decplit: L2
Decplitt mean
Occlusion
0.86
Occlusion abs
KernelSHAP
-0.25
-3.0
Race
Gien der
Baligion
RoBERTa (all)
0.50
0.9
Race
Beligion
Figure 11: Rank correlations between validation set average absolute sensitive token reliance and individual unfairness on the test set for encoder-only models on Civil Comments. The validation set sizes are 500 for race, 500 for gender. and 200 for religion. Higher correlation values indicate greater effectiveness in ranking models. All indicates the model is trained on all bias types.
fairness correlation scores in RQI. We assess the faithfulness of explanation methods using two perturbation-based metrics: comprehensiveness and sufficiency AOPC (Area Over the Perturbation Curve; Younn et al. 2020), computed by masking 5%, 10%, 20%, and 50% of the input tokens. For substitution, comprehensiveness and lower sufficiency scores indicate more faithful explanations.
the [MASK] token in BERT and the [PAD] token in Qwen3-4B. Higher
Our results race bias in Civil Comments (Figure 31 and Table 8) reveal clear link between faithfulness and fairness correlation of explanations. achieve better faithfulness scores than their 1.2-based counterparts, yet they consistently perform significantly worse in identifying bias. We attribute this discrepancy to two key differences hetween the faithfulness metrics and our fairness correlation measure. First, faithfulness evaluates attribu- tion scores across all input tokens, whereas our fairness correlation measure only considers sensitive token reliance. Second, perturbation- baaaeb faithfulness assesses the impact masking tokens on model predictions, while our individual unfairness metric compares predictions when one social group is substituted for another. Taken together, these findings suggest that explanation faithful- ness is not reliable indicator of bias detection ability. We therefore do not recommend selecting explanation methods for fairness on the basis of faithfulness results alone.
particular, mean-based explanations may
J EXPLANATIONS FOR HUMAN FAIRNESS AUDITING
In addition evaluating input-based explanations automatic bias detectors, also examine their ability to support human auditing of biased model predictions. To this end, we conducted small-scale human study following the experimental protocol described below.
32

---

Published as a conference paper at ICLR 2026
Llama3 Instruct
Pank Cormittion
00
Ibaselinel
Religion
ALth rollout
Atth flow
148
Corelittin
IxS moon
IG L2
0.3
Intörad
Intoradi
DeepLitt mear
DeepLift
Gcclusion
Religion
Qwen3-8B
KemeiSHAP
Proginfer
Coreettio
Figure 12: Rank correlations between validation set average absolute sensitive token reliance and individual unfairness on the test for decoder-only models on Civil Comments. The validation set sizes are 500 for race, 500 for gender, and 200 for religion. Higher correlation values indicate greater effectiveness in ranking models.
Table 8: Faithfulness results of different explanation methods on BERT and Qwen3-4B models.
Explanation
Comp. (1)
Suff. (J)
Faimess Correlation (1)
Comp. (1)
Suff. (1)
Fairesss Conrelation (1)
BERT
Qwen3-4B
Atlention
4.50
3,20
0.62
10.34
17.20
0.14
Attn rollout
4.37
3.11
0.59
9.04
15.70
0.02
Attn flow
4.01
3,46
0.47
10.57
16.82
0.04
Grad L2
4.82
2.99
0.50
12.30
16.09
0.32
Grad mean
0.77
6.16
0.06
11.41
17.50
0.06
DecpLiff L.2
4.72
3.09
0.50
12.44
16.17
0.31
DeepLifft mean
1.68
5.75
0.06
10.78
18.69
0.06
4.89
2.95
0.49
12.35
16.27
0.32
IxG mean
7.44
1.70
0.30
0.17
IntGrad L.2
4.81
3,02
0.57
12.33
16.86
0.25
IntGrad mean
10.68
-0.36
0.45
14.21
16.12
0.04
Occlusion
13.16
-0.90
0.62
20.05
13.73
0.27
Occlusion abs
0.79
0.56
0.66
22.48
20.36
0.26
KemmelSHAP
5.99
2.30
0.21
11.49
17.86
0.02
ProgInfer 16.08
DecompX
-2.77
0.40
10.32
17.96
0.025
We randomly sample 48 correctly predicted examples related to race bias from Civil Comments (4*6=24 from BERT and 24 from Qwen3-4B, balanced across all group- categories). We evaluate six explanation methods: three directed methods (Occlusion, IxG mean, KernelSHAP) and three undirected methods (Occlusion abs, IxG L2, Attention), chosen to cover diverse explanation families and performance characteristics observed in RQ1.
For each example, annotators first read the input text and provide their toxicity prediction. They are then shown either the three directed explanations or the three undirected explanations for that example. For each explanation, annotators give two ratings 1-5 scale: one assessing its
33

---

Published as a conference paper at ICLR 2026
Park Corretetoo
0.3
-33
BERT (all)
L3
Park Connisaiion
Val fimess Ibaselinel
Attention
rollout
44
Grad mean
Grad L2
Race
Gender
Religion
Intorad mean
IntGrad L2
Coree wttn
0.5
Occlusio
Oxclusion abs
KemelSHAP
DecompX
Ruligion
RoBERTa (all)
Rank Comelation
Bace
Gender
Malliiion
Figure 13: Rank correlations between validation set average absolute sensitive token reliance and individual unfairness the test set for encoder-only models Jigsaw. The validation set size is 200. Higher correlation values indicate greater effectiveness in ranking models. All indicates the model is trained all bias types.
interpretability, and another evaluating how much the model's prediction appears to rely on race- related bias or stereotypes. based on the information conveyed by the explanation.
After annotation, we collect annotators' perceived bias ratings and measure their correlation with the ground-truth individual unfairness scores. Higher fairness correlation indicates greater effectiveness of an explanation method for human fairness auditing.
Table 9: Fairness correlation of explanation methods for human faimess auditing and their inter- pretability. Best scores in each explanation type are marked in bold. Higher fairness correlation scores indicate that explanations can better assist humans to detect bias.
Undirected IxG L2
Directed IxG mean
Attention
Occlusion abs KemneSSHAP
Occlusion
Fairness correlation Interpretability 2.256
0.402
0.123
0.433
0.254
-0.078
0.342
3.179
2.920
2.518
2.439
2.780
Table 9 shows that certain explanation methods, such Attention, Occlusion, and Occlusion abs, achieve high fairness correlations, suggesting that they can effectively assist humans in detecting bias. Across explanation types, undirected explanations appear more helpful. For example, Occlu- sion and Occlusion abs produce the same attribution patterns that differ only in directional encoding, yet participants thermore, while some methods support both human and automatic bias detection consistently (e.g., Attention and Occlusion abs), others, such as IxG L22 show substantial gaps in performance. This
better able to identify bias using the undirected variant (Oeclusion abs). Fur-
34

---

Published as a conference paper at ICLR 2026
Llama3.
Pank Cormittion
03
Ibaselinel
Religion
Qwen3-4B
Alth rollout
Atth flow
1-8
Corelitton
IxS moon
IG L2
0.
Intōrad
Intoradi
DeepLif mear
DeepLift L2
Gcclusion
Religion
Ooclusion
Qwen3-8B
KemeiSHAP
Proginfeer
Pank Comelttion
Figure 14: Rank correlations between validation set average absolute sensitive token reliance and individual unfairness on the test set for decoder-only models on Jigsaw. The validation size is 200. Higher correlation values indicate greater effectiveness in ranking models.
Table 10: Fairness correlation of explanation methods for human faimess auditing under different conditions. Higher fairness correlation scores indicate that explanations can better assist humans to detect bias. Green (red) indicates the results are better (worse) than the baseline (all examples).
Undirected IxG L2
Directed
Attention
Occlusion abs
KerneISHAP
IxG mean
Occlusion
examples Correct predictions Only
0.402
0.123
0.433
0.254
-0.078
0.342
0.572
0.202
0.602
0.217
0.029
0.451
Toxic examples only High interpretability (score
0.637
0.118
0.374
0.231
0.134
0.315
0.138
0.227
0.288
0.154
-0.043
0.404
highlights a potential discrepancy between how humans interpret explanations and how our auto- matic pipeline evaluates them.
We also observe that high interpretability alone does not guarantee better support for human fair- ness auditing: methods with strong interpretability scores (e.g., IxG L2) still fail effectively help humans detect bias. Finally, 4 out of annotators reported that undirected explanations helped them detect bias more effectively, noting that they introduce less noise and make annotation easier.
Table TO further analyzes explanation-assisst human fairness auditing under different conditions. For correctly predicted examples, explanations generally provide stronger support for bias detection. However, the effects of label type and explanation interpretability appear more nuanced and vary across methods. Overall, these results suggest that explanation-assiseer human fairness auditing is promising and interesting direction for future work and warrants further investigation.
K HYBRID BIAS MITIGATION TECHNIQUES
we conducted preliminary experiments that combine several pre- procesicssiing techniques (group bal- ance, group-class balance, and CDA) with effective explanation-hased debiasing method (IxG
35

---

Published as a conference paper at ICLR 2026
BERT
Relgion
BERT (a
Val faimess (baseline)
Random
Attnn flow
Gender RoBeRTa
Religion
leG mean
bG L2
Initürad
2.6
Deeplift mean
DeepLift L2
Occlusion abs
Kerne SHAP
Decompx
0.
Race
Gender
Religion
RoBERTa (all)
2.0
02 Race
Gender
Religion
Figure 15: MRR @ results for encoder-only models on Civil Comments. The validation sizes are 500 for race, 500 for gender, and 200 for religion. Higher MRR @ scores indicate explanations are more effective selecting the fairest models. All indicates the model trained bias types.
L1/L2). The resulting individual fairness outcomes, along with comparisons traditional and explanation-only methods, are presented in Table 11
Table 11: Each cell shows the Avg. score after applying a combination of pre- pee-processing and explanation-be based debiasing methods. Lower values indicate reduced bias. Values in parentheses denote the change relative to using only the corresponding rraditioallexxlaaation- where negative values indicate improved debiasing. We observe that hybrid approaches consistently achieve stronger bias mitigation than either method used in isolation.
method,
Race
Group balance
Group-class balance
CDA
IxG LI
0.012 (-4.92/--1.411
0.000 1-3.008/-1.44 1,73))
0.001 (-0.547/-1.473) 0.210 (-0.338/-2.550)
IxG L.
2.162 (-2.342/-0.598)
2.110 (-0.938/-0.650)
Gender
Group balance
Group-class balance
CDA
IxG LI
0.005 (-0.594/-0.548)
0.001 (-00833/-00552)
0.001 (-0.488/-0.551) 0.368 (-0.122/-0.271)
IxG L2
0.308 (-0.291/-0.31))
0.546 (-0.292/-0.093)
We observe that the hybrid method achieves better bias mitigation effects than using each debiasing method alone, with consistent improvements for both race and gender bias. Based on this, we believe exploring hybrid methods for more effective bias mitigation could be promising future direction.
36

---

Published as a conference paper at ICLR 2026
Liama 3.2-3 3.2-3B-Instrucc
2.0
02
faimess (baselinal
Religion
Random
Attention
Qwen3-48
Attnn rolleut
mean
Grad 12
IxG mewn
Inttrra mean
Inttradd L2
Deeplitt
DeepLit
0.0 Bace
Gender
Rellgion ion
Occlusian
Occlusion abs
Owen3-88
Proginfer
2. Race
Gender
Religion on
Figure 16: MRR @1 results for decoder-only models on Civil Comments. The validation set sizes are 500 for race, 500 for gender, and 200 for religion. Higher MRR @ scores indicate explanations are more effective selecting the fairest models.
L FAIRNESS CORRELATIONS IN EXPLANATION-DEBIASST MODELS
presents the fairness correlation scores computed on explanation-debiased models. We find that Grad L2, IxG L2. DeepLift L2, and Occlusion-base explanations still show strong bias mitigation ability in the debiased models.
Figure 32
M LIMITATIONS AND FUTURE WORK
Our study has several limitations that we acknowledge and aim to address future work.
First, the first quantitative investigation of this topic, study focuses solely hate speech detection and uses a limited set of experimental setups. Although the results are consistent across these setups and preliminary experiments (Appendix HH) suggest good generalization across tasks, models and sensitive token vocabulary, broader validation is still needed. Future work could extend this evaluation additional domains and high-stakes applications.
Second, several findings are derived under the specific experimental setups used in this work. For instance, in RQ2, we conclude that the proposed attribution-based metrics ness indicators. However, it remains possible that other metrics could be effective. Similarly, our fairness balanced metric in RQ3 may not the optimal validation strategy all settings. As is infeasible to exhaustively enumerate and evaluate all potential configurations, we believe our con- clusions nonetheless offer valuable guidance and highlight important methodological considerations for the community.
reliable fair-
Third, our work focuses on evaluating standalone explanation-bass strategies for improving fair- ness. Ensembles of multiple explanation methods, hybrid approaches that combine explanation techniques with established debiasing methods, may yield better outcomes. Additionally. incorpo- rating human oversight may further enhance the effectiveness and robustness of explanation- based fairness auditing. Our preliminary experiments show promising results in using hybrid debiasing techniques K and demonstrates the possibility for human fairness auditing based explanations]]
37

---

Published as a conference paper at ICLR 2026
BERT
Relgginn
BERT (all
Val faimess (baseline)
Random
Attn flow
32
Gender RoBERTa
Religion
beG mean
bG L2
Inttürad
Deeplift mean
DeepLift L2
Occlusion abs
Kerne SHAP
Decompx
0.
Race
Gender
Religion
RoBERTa (all)
02 Race
Gender
Rellgion in
Figure 17: MRR@ I results for encoder-only models on Jigsaw. The validation size 200. Higher MRR @ scores indicate explanations are more effective in selecting the fairest models. All indicates the model is trained on all bias types.
Based on that, we believe that a systematic investigation of such hybrid or human-in-the-loop ap- proaches represents interesting avenue for future work.
Fourth, we do not identify any explanation method that consistently outperforms others across all research questions, which prevents us from offering a single recommendation. We therefore en- courage future researchers to choose explanation methods that align with their specific tasks and constraints. Future work could further investigate why certain methods are better suited to particular settings and, ideally, develop practical guidelines for selecting effective methods without requiring extensive empirical comparisons.
Finally, although we consider both group and individual fairness, this work provides a more in-depth analysis of individual fairness (in RQI and RQ2), driven by the conceptual alignment between input- based explanations and individual fairness notions. We encourage future work to more thoroughly examine how explanation methods relate to group fairness.
N LLM USAGE
Apart from models evaluated our experiments and analyses, we used LLMs (ChatGPT) solely to polish the writing in this work.
38

---

Published as a conference paper at ICLR 2026
Liama ii-ssutt
92
Vel faimess (baseline)
Religion
Random
Attention
Owen3-48
Attn rolluut
2.6
flow
Grad
lx mean
IntGrad L2
Deeplift mean
Deeplit
Gender
Religion inn
Occlision
Occlusion abs
Kerne SHAP
Proginfer
Race
Gender
Ralig inn
Figure 18: MRR@ I results for decoder-only models Jigsaw. The validation size 200.
Higher MRR @ scores indicate explanations are more effective in selecting the fairest models.
Race
Istection
toee Good
Groe
HO
Acvetton
Goot
by Deckottn
Atantion
five
bo Decistion
Attertien
cood
her
NO Geciattoe
aith
Gender
Attention
Aovetton mo Aooe
Ateetton
woter
Relgion
Gar
Lt
H
Mlcotton volout
How
Clae Ld
C2
Actontion
otlius
Aow
Coal d
L2
u
Dridotion
Amartion
riiend
Nlte
L2
Orighal Accuvty
Origral
Figure 19: Accuracy and faireess results for bias mitigation in BERT on the Civil Comments dataset, using different explanation methods during training. For consistency with accuracy, fairness results are reported 100- (Dispace Dispigr Disp Avg,), so that higher values indicate better debiasing performance. Each column corresponds to models selected by maximizing the faimess- balanced metric with respect to the indicated bias metric.
39

---

Published as a conference paper at ICLR 2026
Race
DiPpcc
Disptr
Dispyv
Asterti
Grech
ootetie
Ma to
Gender
Baligion
Arteation
Gi
wau wo
Graano
Attrotion
quammin
Atention
Jlto rolit
belLd
Grchason
Astuttion
ane Rre
Gree 6014
Atte
Figure 20: Accuracy and faimess results for bias mitigation in RoBERTa on the Civil Comments dataset, using different explanation methods during training. For consistency with accuracy, fairness results are reported 100 aDispace DisPipr Disp Avgn so that higher values indicate better debiasing performance. Each column corresponds to models selected by maximizing the fairness- balanced metric with respect the indicated bias metric.
Raze
Dispac
DPpp
Disp
Avg.
an no poriaien
Atrention con
Amm Ape comd
cood
Atrentionn aam callot
ann dow
cood
aodd
bio sociation
Are wnn
Gender
man
Atarttion
Lor Gottate
Attetton ritrrl
tow
aod God
God
Lo Dorkatan
Anaethon
ttr Pettuiso
Deen La
Cret te po
Gatkttor
aste redtor
Art
credt Cieed
po
Orokazan
Artuntion
fove
Gred
Gred to
La
Oacheito
Attertion
and mo11 you
La
Figure 21: Accuracy and faimness results for bias mitigation in BERT on the Jigsaw, using different explanation methods during training. For consistency with accuracy, fairness results are reported as 100 (Disp Disp Disp Avg,, } that higher values indicate better debiasing performance. Each column corresponds to models selected by maximizing the fairness faimess-balanced metric with re- spect to the indicated bias metric.
40

---

Published as a conference paper at ICLR 2026
DiSp
Dispie
DiSper
Ava.
HoL Dethaas
Rive Good LI
Gost
Attentinn
Attrodlina
Goof
bo Declattn
cond
Lnd
Gender
attosten
Doad
nnd Oniintio
Actetion
tnd aodd
Decistion
Attenton
L2
Relgion
Li
L2
Attetivn
Good
Gool
Ixf
aula
Dectooon
Attentono eotort
Ease
cooet
La Aet Ao
Figure 22: Accuracy and fairness results for bias mitigation in RoBERTa on the Jigsaw dataset, using different explanation methods during training. For consistency with accuracy. fairness results are reported 100 (Dispace DisPipe DisP AvgiJ, so that higher values indicate better debiasing performance. Each column corresponds to models selected by maximizing the faimess faimess-balancee balanced metric with respect the indicated bias metric.
DisPuc
Disbyr
Dispyr
Arg.
Cade S00s
Delaul
Tedittonal
Expllnat Ameund
Figure 23: Harmonic mean between accuracy and fairness for established debiasing methods and explanation-ban methods BERT on Civil Comments. A higher score indicates better balance between model performance and fairness.
41

---

Published as a conference paper at ICLR 2026
532
Caadl Sides
Tosiiiona dobkasing
Figure 24: Harmonic mean between accuracy and fairness for established debiasing methods and explanation- basedd methods for RoBERTa on Civil Comments. A higher score indicates better bal- ance between model performance and fairness.
0kdd D001
debiasing
Figure 25; Harmonic mean between accuracy and fairness for established debiasing methods and explanation-batt methods for BERT on Jigsaw. A higher score indicates better balance between model performance and fairness.
42

---

Published as a conference paper at ICLR 2026
D-ppect
Dishe
Disper
Figure 26: Harmonic mean between accuracy and fairness for established debiasing methods and explanation-based methods for RoBERTa on Jigsaw. A higher score indicates better balance between model performance and fairness.
Biy
mist
FnGrad mann
Ower3
Rinjibr
Sinn Ficant
Buti
Figure 27: Fairness correlation results for race bias on Twitter Sentiment with Lllama3.2-3B-nmm and Qwen3-4B. Higher values indicate that the method is more effective and reliable in detecting biased predictions at inference time.
43

---

Published as a conference paper at ICLR 2026
Ulama32--3
Attn rellout
0.25
Altn flww
Gred
0.23
IGG
Inttiind mean
IntGred 12
Deepl
0.00
Deoplitt
Declusion
KarnelHHke
Proginter
sigrificant
significant
Figure 28: Fairness correlation results for race bias on Civil Comments with Llama3.2-31 and Wwen2.5-3B-Instruee Both models are differently aligned from models in our main experiments. Higher values indicate that the method is more effective and reliable detecting biased predictions at inference time.
Falmess Correlation 22
Aternton
Altn rellont
Altn Rew
Grad mont
Grad
KG main
Incordd mean
insirad
DespL meen
Desplit
Oeskaion ats
KomeiSHAP
Decmpp
Qwen3-iB
Falmers Corneiation
0.2
Attentionn
ralout
Alin
Grail mear
Grad
mean
Intirad menr
Intdred
Despdlff meser
Derpputt
Chdiaiier
Ocaturion
Kemathia
Prosinder
vorabidary Coverage
coverage
Figure 29: Fairness correlation results when using reduced sensitive token vocabulary for reliance computation. Results are reported for gender bias on the Civil Comments dataset. Fairness metrics are still computed using the full vocabulary. The reduced vocabulary size has only marginal effect on fairness correlations.
44

---

Published as a conference paper at ICLR 2026
Fail
DApa
Dispy
Acourac)
Figure 30: Fairness and accuracy results for gender bias mitigation with a reduced sensitive token vocabulary. Each column corresponds to models selected by maximizing the bala-ce metric with respect to the indicated bias metric. Using an incomplete vocabulary yields slightly worse debiasing performance than using the complete vocabulary, while preserving task performance more effectively. Overall, the impact reduced vocabulary coverage is minimal.
Faimess Cordlatoo
0,7
Ownr3-4R
0.7
Owne3-1
0.6
0.5
0.5
0.4
0.3
20 Comprahansiveness (.))
15
23
15
20
Sufficiency (4)
Figure 31: Faithfulness and fairness correlation results of different explanation methods. No clear relationship between explanation faithfulness and their bias detection ability is observed. Each point represents the faithfulness and fairness correlation of one explanation method applied to default/zero-stee models.
45

---

Published as a conference paper at ICLR 2026
1.0
9.2
0:39
0.59
0.35
0.62
0.37
flow 0.38
0.
0.40
0.49
0.14
0.5
0.26
0.12
0.34
0.8
Debiasing Method (RQ3)
Altn rollout
0.17 0.089
0.02
0.31
0.67
0.55
0.67
0.64
0.94
0.33
0.62
0.4
0.47
2.15
0.5
0.6
0.19
0.64
0.57
0.64
0.53
Grad 0.44
0.28
0.088
0.56
0.63
0.092
0.59
0.047
0.070
0.56
0.4
1&G L1
0.36
0.33
0.28
9.15
0.46
0.19
0.45
0.65
0.7
0.18
0.38
0.32
0.28
0.15
0.56
0.13
0.35
0.2
Occlusion
0.35
0.26
0.16
0.49
0.49
0.46
0.17
0.
011
0.37
Attention
Attn rolleut
Aldn flo
Grad mean
mean
Occtusion
Ocatusion aba
Deeplit mean
Deeptitt L2
Intraad mean
KomeiSHAP
Decompx
0.0
Gradd
IGG
IntGrad
Explanation Method (RQ2)
Figure 32: Fairness correlation results on BERT models with race bias mitigated through explanation baased methods on Civil Comments.
46


Reviews:
[{"review_id": "bhidT68iI7", "reviewer": "Reviewer_K4TD", "summary": "The paper examines the relationship between input-based explanations and fairness in the context of hate speech detection. The authors show that the tested XAI techniques can effectively identify biased predictions in both encoder-only and decoder-only models, and can also assist in guiding bias mitigation.", "questions": "It would be valuable to discuss the generalizability of the proposed approach beyond hate speech detection. To what extent can the methods and findings be applied to other NLP tasks or domains? Clarifying this would help readers better understand the broader applicability and impact of the work.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 1, "strengths": "The paper is well-written and easy to read, providing all the details necessary for reproducibility in the appendices. (On this point, I suggest double-checking that all appendices are properly referenced in the main body of the paper.)\nThe experimental setup is comprehensive, as the authors evaluate multiple models, datasets, XAI methods, and fairness techniques.\nThe research questions addressed in the paper are compelling, and the contribution is well-positioned at the intersection of XAI and fairness, a space where existing literature remains limited.", "weaknesses": "Consider expanding the motivation for incorporating both fairness and explainability in hate speech detection, particularly in the Introduction and Related Work sections, to better contextualize the importance of these aspects.\nIt would be helpful to further elaborate on the rationale behind focusing specifically on input-based explanations.\nPlease clarify the distinction between hate speech detection and abusive language detection. While the study focuses on hate speech, the labels in Notations (toxic vs. non-toxic) appear to align more closely with the abusive language perspective. Again in Notations it would be useful to explicitly define what is meant by social bias. Additionally, the sensitive token reliance scores would benefit from clearer motivation and stronger connections to related literature. For instance, is this a new measure introduced by the authors, or is it adapted from prior work? Furthermore, when the hateful nature of a sentence stems directly from a sensitive token, how should reliance on that token be interpreted? Clarifying these aspects would strengthen the reader’s understanding of what the metric captures.\nThe contributions of the paper could be stated more explicitly. If the main contribution is an evaluation framework, it might be helpful to revise Section 3 to better highlight its novelty and unique aspects. Moreover, including a visual overview or workflow diagram of the experimental setup could greatly improve clarity and accessibility.\nConsider adding a short Future Work section to outline possible extensions. Additionally, frequent use of red and green in tables can be visually distracting: using bold text to indicate the best results might improve readability. Some figures could also be refined to enhance their legibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hV5V3dpwtg", "reviewer": "Reviewer_MpuR", "summary": "This paper presents a systematic study with a focus on the intersection between fairness and explainability, both central topics in AI ethics. Oriented by three clearly defined research questions, the authors explore the utilities of SOTA feature attribution methods, a particular form of explanations, in machine learning fairness for NLP. Based on the empirical observations, in-depth discussions are carried out, which bring together the standard fairness metrics and the new explanation-oriented perspective. The discussions deliver new insights that provide potential guidelines for future practice in debiasing language models.", "questions": "I do not have particular questions regarding the content, as the presentation of the work is clean and clear. Instead, I would like to establish a discussion with the authors, so the questions below serve more for the purpose of brainstorming.\n\n- What do the authors think about the observations for RQ2 beyond the takeaway? Indeed, there are mismatches between the statistical and attribution perspectives; however, directly determining what metrics are responsible is non-trivial. Particularly, there could be “implicit” biased attributions that are exempted from the standard fairness metrics. By “implicit”, I refer to the predictions that are correct but contain falsely attributed features. The current fairness metrics do not consider such implicit biases as long as the prediction results are balanced among demographic groups. These implicit biases can be a problem when attributions to other features are not strong enough to cover them.\n- Could a cross-validation be helpful in further revealing the mismatches? For example, by quantifying implicit biases in terms of attributions for correctly predicted samples.\n- Could the authors share some thoughts about combining traditional debiasing approaches (the data augmentation perspective) and explanation-driven approaches?\n- Since this paper considers different bias categories, have the authors observed any internal connections among different bias categories? A previous work reported indirect impacts during debiasing [1], and I’m wondering whether that is observed in the broader settings considered in this work. Particularly, does debiasing on one category also suppress other forms of bias? Or, does it transfer the biases to other categories while explicitly regulating for one specific type?\n\nThe presented work appears complete as it is; yet, deepening the investigations and discussions will certainly enhance its impact.\n\n[1] Cai, Yi, et al. \"Power of explanations: Towards automatic debiasing in hate speech detection.\" *2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)*. IEEE, 2022.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- This paper presents decent work that is clearly motivated and smoothly unfolds the storytelling, following three research questions that are inherently connected.\n- The experiments are well structured, with reasonable choices for the fairness metrics and a collection of feature attribution methods that represent SOTA.\n- The studies that compare the (traditional) statistical perspective with the explanation-driven perspective are insightful; a conclusion is reached from each research question, presented in the form of takeaways.", "weaknesses": "- The experiments mainly rely on existing methodologies. Although the authors bring standard and explanation-driven debiasing techniques to the same table, no further exploration is conducted for possible combinations across categories.\n- While the deliveries are generally insightful, the observations for RQ2 are not sufficiently discussed, rendering a rather hasty conclusion (see the first two points in questions for more details).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ge3p8yhMxB", "reviewer": "Reviewer_7Yw8", "summary": "The paper does a very nice job scoping the problem and running a broad comparison of 14 explanation methods across two hate-speech datasets and multiple bias types. It also delivers clear, actionable takeaways for practitioners, notably that certain explanation families (occlusion, L2-like gradients) are good local bias sensors and can supervise debiasing, and that explanation signals can be more useful than an LLM-as-a-judge for this task. However, the core claim the paper wants to make, “input-based explanations can support fairness operations in hate speech detection”, rests on a data and measurement setup that is still quite narrow: two toxicity datasets, fixed identity vocabularies, and counterfactuals built via simple token replacement. Furthermore, the paper itself presents a negative result on model selection but does not delve into why explanations fail in this context, nor does it conduct the stronger causal/feature-removal tests that would strengthen the attribution-model-reliance link.", "questions": "Q1: What's the cost/latency for the 14 explanation methods (time per 1k examples, hardware) to support practicality claims?\n\nQ2: Will the authors be able to add a small human/practitioner evaluation (5–10 cases) on whether the explanations are actionable for fairness auditing?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "S1: This is a relatively large, well-scoped empirical study that ties input-based explanations directly to fairness in hate speech detection, a gap that existing work had mostly treated qualitatively.\n\n\nS2: Clear question framing that makes the contributions easy to map to real moderation pipelines.\n\nS3: Systematic comparison of 14 explanation methods from different families (attention, gradients, occlusion, SHAP), providing an unusually comprehensive view of which types of attributions are useful for fairness.\n\nS4: Consistent positive result for bias detection: occlusion and L2-style gradient attributions correlate well with individual unfairness across settings.\n\nS5: Demonstration that explanation-based regularization can improve several fairness metrics without collapsing task accuracy, showing a concrete way to operationalize the insight.\n\nS6: Practical comparison showing that explanation-based signals can outperform an LLM-as-a-judge approach for this task, which is directly actionable for current LLM pipelines.", "weaknesses": "W1: Although the study is broad within hate speech detection, it is still confined to Civil Comments and Jigsaw, with only three bias types and a fixed identity vocabulary, so it is unclear how far the conclusions extend to other NLP tasks or richer identity expressions.\n\nW2: Sensitive-token detection relies on predefined identity terms, so paraphrased, code-mixed, reclaimed, or multiword identity mentions that arise in practice may be undercounted, potentially underestimating model bias.\n\nW3: The individual-unfairness construction depends on counterfactuals built via simple token replacement and balanced subsampling; while workable, this can introduce measurement artifacts that partially blur how much of the signal comes from explanations vs. from the counterfactual setup.\n\nW4: While the paper does include decoder-side experiments and an LLM-as-a-judge comparison, the decoder evaluation is still relatively thin and focuses on smaller instruction-tuned models, so it is uncertain whether the findings hold for larger or differently aligned LLMs.\n\nW5: Sensitive-token reliance is hard-coded to a particular aggregation rule (max over sensitive tokens); alternative aggregations (sum, top-k, span-aware) are not compared, so part of the conclusion may be an artifact of this choice.\n\nW6: There is no human-in-the-loop or practitioner-style evaluation to show that the surfaced explanations are actually interpretable and actionable for fairness analysts.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xxHgAQ2iPe", "reviewer": "Reviewer_EN8y", "summary": "The paper presents a quantitative study of the relationship between input-based explanations and fairness of NLP models in the context of hate speech detection. It focuses on three aspects in particular: (1) identifying biased predictions using input-based explanations, (2) selecting fair models based on input-based explanations, and (3) mitigating bias during model training using input-based explanations. The authors find that input-based explanations are effective at detecting biased predictions and reducing biased predictions of NLP models during training but are not suitable as a selection criterion for fair models.", "questions": "Based on the mentioned weaknesses, two questions can be raised to the authors:\n\n1. How can the results from section 6 (i.e., that input-based explanations outperform LLM-as-a-Judge) be expected to generalize to other, particularly larger and state-of-the-art, LLMs?\n\n2. What are the limitations of the study that should be addressed by future work?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Originality: The paper appears to be the first systematic quantitative study of the relationship between input-based explanations and fairness of NLP models. The paper also investigates a broad set of 14 input-based explanation methods across 4 models (2 encoder-only and 2 decoder-only models) on 2 hate speech detection datasets.\n\nQuality: The metrics used for measurement of bias/fairness are thoughtful and seem to be good approximations of model fairness. While the chosen method is not highly complex, it is well-executed and effective at measuring bias/fairness of models and their predictions and determine suitability of different input-based explanations for detecting model bias.\n\nClarity: The explanation of the approach and its relevance as well as the presentation of results are very clear. All concepts (e.g., sensitive tokens, attribution scores, sensitive token reliance scores, etc.) are properly introduced with both a mathematical definition and a natural language explanation. The overall argumentation and section structure makes sense and has a logical flow, always leaving the reader aware of what (e.g., which research question) is being discussed in any particular paragraph. The paper also makes use of suitable visual elements such as the takeaway boxes briefly summarizing the answers to the research questions.\n\nSignificance: The three research questions raised by the paper are of high significance to the research community and motivate further research into improving model fairness using input-based explanations. The paper also presents a very comprehensive related work section, pointing to other relevant work on related aspects.", "weaknesses": "As highlighted in the strengths, the paper is generally very well-written and presents rigorous research, leaving only a few minor weaknesses:\n\nSome figures take a while to fully understand, given the large number of methods, metrics, and settings shown. For instance, in Fig. 2, the caption mentions that no model outperforms the baseline and it is not immediately apparent which bar or line represents the baseline. This becomes clear only after reading the accompanying text mentioning the “individual unfairness on the validation set” as the baseline and then looking back at the figure, where the legend includes one line “Val fairness”. Possibly, the authors could simply adjust the legend entry to “Val fairness (baseline)” to make this clear. Further, the visual style of the “Val fairness” line is different between Figs. 2 and 3 and could be harmonized. Fig. 4 uses the terms “Accuracy” and “Fairness” in both the legend and on the chart axes. One must read the charts very carefully to notice that each subplot has two y-axes, one for accuracy and one for fairness, which follow slightly different scales.\n\nIn section 6, the paper presents the important finding that input-based explanations outperform an LLM-as-a-Judge method on bias detection. This would be a highly significant finding given the current prevalence of LLM-as-a-Judge methods. However, the analysis is only based on a Qwen3 LLM judge (presumably the same 4-billion-parameter Qwen3 LLM used for the other experiments), which is a relatively small LLM and cannot be expected to reach the impressive performance levels of state-of-the-art LLMs. While potential budget constraints might make this choice understandable, it limits the generalizability of the finding to LLM-as-a-Judge and LLMs overall.\n\nAcross most experiments, the study’s results indicate no consistently best method. While this lies in the nature of empirical research, the practical implications of the findings are not always clear. In the conclusion, the authors claim that they “provide practical recommendations on which explanation methods are best suited” (lines 483f.) but the given recommendations fall somewhat short of being practical and useful. As a practitioner or researcher, it is not always clear what method should be used based on the findings of this study. At the very least, I suggest that the authors add a section discussing the study’s limitations and outlining future work to come closer to such practical recommendations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)