FaAlGrad: Fairness through Alignment of Gradients across Different Subpopulations
Authors: Nikita Malik
Konda Reddy Mopuri
Venue: Accepted by TMLR ()

Abstract: The growing deployment of Machine Learning systems has increased interest in systems optimized for other important criteria along with the expected task performance. For instance, machine learning models often exhibit biases that lead to unfair outcomes for certain protected subpopulations. This work aims to handle the bias in machine learning models and enhance their fairness by aligning the loss gradients. Specifically, leveraging the meta-learning technique, we propose a novel training framework that aligns the gradients computed across different subpopulations for learning fair classifiers. Aligning the gradients enables our framework to regularize the training process, thereby prioritizing fairness over predictive accuracy. Our experiments on multiple benchmark datasets demonstrate significant improvements in fairness metrics without having any exclusive regularizers for fairness. Thus our work contributes to developing fairer machine learning models with broader societal benefits.

Published in Transactions on Machine Learning Research (01/2025)
FaAIGrad: Fairness through Alignment of Gradients across Different Subpopulations
Nikita Malik
nikitemallkk23033gmmaaa com
Department of Information Technology
Manipal Institute Of Technology, Manipal
Konda Reddy Mopuri
Armopuri httptrriiaiiit.....om atth. ac.in
Deportment of Artificial Intelligence Indian Institute of Technology Hyderobod.
Reviewed on OpenReview: https: oppenreveew net/ forum? id= K4Am waHl
Abstract
The growing deployment of Machine Learning systems has increased interest in systems optimized for other important criteria along with the expected task performance. For in- stance, machine learning models often exhibit biases that lead to unfair outcomes for certain protected subpopulations. This work aims to handle the bias in machine learning mod- els and enhance their fairness by aligning the loss gradients. Specifically, leveraging the met meta-learning technique, propose novel training framework that aligns the gradients computed across different subpopulations for learning fair classificrs. Aligning the gradients enables our framework to regularize the training process, thereby prioritizing fairness over predictive accuracy. Our experiments on multiple benchmark datasets demonstrate signifi- cant improvements in fairness metrics without having any exclusive regularizers for fairness. Thus our work contributes to developing fairer machine learning models with broader so- cietal benefits. Sample code for implementing the proposed framework is available at this link
1 Introduction
Machine lcarning models have become integral to numerous real-world applications, from credit approval and hiring decisions personalized recommendations and medical diagnoses. However, the widespread deployment of these models has raised concerns (e.g., Miller (2020): Stevenson (2018)) about their fairness and potential biascs, particularly when used in high-stakes decision-making processes that impact individuals' lives and opportunities. Biases in machine learning models stem from various sources, including biased training data, algorithmic design choices, and societal prejudices. Such biases can lead to discriminatory outcomes, disproportionately affecting certain demographic groups and reinforcing existing inequalities.
The need for fair and unbiased machine learning models has recently gained significant attention in academia and industry (e-g., Barocas al. (019);; Chouldechova & Roth (2018); Zliobaite (2015): Romei & Ruggieri (2013)). Ensuring fairness in machine learning a critical ethical concern and has become growing area of research. Researchers and practitioners have explored various fairness-aware learning techniques to mitigate bias and promote equitable decision-makiin with predictive accuracy, many fairness-aware methods tend to compromise model performance. Various approaches have been proposed to address this issue, including pre-processing techniques (e.g., Sun el al.
However, achieving fairness often entails trade-off
(2022); Madras et al. (2018)), pnoorssssing lairness-wward algorithms (e.g., Biswas Rajan 20021) Kamiran
& Calders (2012)), ) and post-processing mothods (c.g., Nandy al. (2022): Kim al. (2018)). However,
these traditional methods often fail to comprehensively tackle the challenge of bias reduction, they treat fairness isolated concern rather than core aspect of the learning process.

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In this paper, we identify specific notion of bias in machine learning and present framework to alleviate it. While instances of dataset bias and feature (variable) bias are prominent, we acknowledge the broader spectrum of biases that infiltrate machine learning models. Biases can manifest from various other factors including biased sampling, algorithmic design choice and evaluation biases.
Dataset bias: if the datasct used for training the machine learning model does not represent the real-world population faithfully, the resulting model will likely be unfair to certain subpopulations.
Feature bias: if the model considers certain features more important (e-g,, for predictive accuracy), it may focus more on them to the detriment of other relevant features. Often feature bias is a consequence of dataset bias.
Therefore, one way to examine these biases is that they result from the dominance of specific subpopulations (or their representative feature attributes) during the learning process. We hypothesize that carefully allevi- ating that dominance can positively affect the fairness of the resulting machine learning systems. This paper proposes novel gradient descent-driven training framework to align the loss gradients computed across different subpopulations in the training dataset. Aligning the gradients acts as a check on the bias-causing dominance of the responsible subpopulations. This, in turn, gives importance to samples of all the subpop- ulations. Often, the subpopulations are classified as protected or unprotected based on the values assumed by the sensitive fcatures. This classification depends on various factors such as the task, distribution of the data samples and their targets, etc. is important to note that the proposed method is particularly well suited for addressing feature and dataset biased in machine learning models.
The proposed approach achieves gradient aligument in the framework of mela-learning. Specifically, it is inspired by the Model-Agnostic Meta-Learning (MAML) Finn et al. (2017). a popular meta-learming algorithm that has demonstrated impressive performance across various applications. It enables models to acquire knowledge from multiple tasks and generalize effectively to new tasks. We leverage the flexibility of MAML and adapt it to address fairness concerns. Gradient alignment achieved (refer to sec. 4.1 by choosing different subpopulations of the training data as separate tasks within the meta-learning framework, leading to the learning of fair models.
The objective of fair learning is to ensure similar model behavior toward both protected and unprotected subpopulations. The proposed approach suppresses the unwanted dominance caused by any subpopulations by aligning the loss gradients computed from both subpopulations. In other words, it avoids the potential interference of these gradients, resulting in a biased model. Specifically, the proposed approach treats the protected and unprotected subpopulations as the meta-train and meta-test tasks, re- spectively (refer to sec. 3.1
To evaluate the effectiveness of our approach, conducted extensive experiments four well-known clas- sification datasets: the compas, Communitics and Crimes, and the Adult datasct. These datasets are widely used to study fairness issues in machine learning. The experiments measure fairness metrics, such as de- mographic parity and equalized odds (refer to sec. 3.2). along with classification accuracy. The results of our experiments demonstrate that our mota-learning-inspired approach leads to significant improvements fairness metrics, indicating reduction in bias.
In summary, the key contributions of this paper are follows:
We propose novel meta-learning inspired training strategy for mitigating bias in gradient descent- driven classifiers. It improves fairness by aligning the loss gradients computed across different sub- populations in the training dataset.
We validate the effectiveness of the proposed approach in mitigating bias by evaluating on multiple real-world datascts.
We demonstrate that the proposed method offers flexibility in achieving different levels of fairness and accuracy (refer to sec. 5.5). We can exercise a controlled trade-off between them by manipulating the proportions of the subpopulations in the MAML framework.
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01 fa - 77 Va ((x 84)
04
02
0-11~
04 Fg,, 91..
01
Inner Loop
Outer Loop
An overview of our meta-learning driven framework used to improve fairness. The green area denotes the inner loop meta-train stage, while the purple area denotes the outer loop the mela-test stage. Note that multiple inner loop iterations (I) are illustrated along with their update terms.
Figure 1:
In the following sections, we review cxisting literature on fairness-aware learning methods. This will be followed by an introduction to MAML. algorithm and various fairness metrics used the paper. Subsequently, we will discuss the proposed meta-learning driven framework. Later, we present the experimental evaluation.
2 Related Works
To improve the fairness in machine learning models, several approaches have been designed inspired by the foundational work of Agarwal et al. (2018). These strategies involve Lagrangian relaxation and innovative validation optimization techniques, collectively contributing to the advancements of fairness- aware ma- chine learning models. This approach framed the challenge of achieving accurate classifiers under fairness constraints as two-player zero-sum game. The underlying concept involves employing Lagrangian relax- ation to handle the constrained optimization task. The first player, referred to as the 0-player, fine-tunes the model's parameters by optimizing the objective function using the current Lagrange multipliers. Conversely, the second player, denoted as the A-player, approximates the most robust Lagrangian relaxation through iterative updates to the Lagrange multipliers.
To mitigate the risk of overfitting fairness constraints, Cotter et al. 2019 devised strategy in which the X- player refines the Lagrange nultipliers based on fairness violations quantified using a distinct validation set, rather than relying on the training set itself. Meanwhile, Mandal et al. (2220) proposed a unique approach where the A-player employs lincar programming techniques to compute the maximum potential fairness violation across various re-weightings of the training dataset. This method falls under Distributionally Robust Optimization (DRO) techniques. [Maheshwari & Perrot (2023) uses constrained optimization and re-weighting techniques enforce fairness. It minimizes error rates while adhering to fairness constraints using Lagrange multipliers and focuses on deriving single optimal model, updating Lagrange multipliers via projected gradient descent and optimizing model parameters using weighted sum of group-wise error rates.
After the introduction of MAML Finn et al. (017). apart from numerous other applications, it has also been used in enhancing fairness. Along similar lines, the work of [llack al. (2020) delves into the scenario where a fair model might display unfair behavior when deployed to scparate places in the real world. This study introduces two notable contributions: Fairness Warnings and Fair-MAMI. Fairness Warnings aim to predict if covariate shifts will lead to violating fairness. To achieve this, new training data is generated from the same distribution by introducing mean shifts in the features, and the model is trained to predict violations based on shifts in the features. Conversely, Fair-MAML focuses on training a model to a new task with minimum data. This is realized through using fairness regularizers for Demographic Parity and Equal Opportunity in the loss function of the MAML framework.
Wang al. (2022) deals with the issue of fairness in information retrieval systems. It proposes Meta- learning based Fair Ranking (MFR) model which aims to mitigate bias for protected groups through
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automatically-weighhee loss function. It uses meta learning framework that involves training meta learner from an unbiased sampled aatasst(mela-datase)) while simultaneously training a listwise learning- (o-ra)k(LTRR model on the biased dataset. The meta-learner acts as a weighting function that guides the ranking loss to focus more on the minority groups, thus enhancing fairness. MFR model is formulated as a bilevel optimization problem, where the meta-learner and the ranking model interact to optimize the fairness related paramoters.
In Zhao et al. (021b), framework termed as Primal-Dual Fair Meta-Learning (PDFM) is designed to enhance fairness in few-shot meta-learning scenarios. PDFM operates by learning suitable primal and dual parameters, thereby enabling the model to quickly adapt to new fairness learning tasks. Unlike MAML, where parameters update takes place in outer loop, in PDFM a pair of primal-dual meta parameters are updated through dual decomposition. been introduced. FFML. learns effective priors for a fair classification model's primal and dual parameters, which govern accuracy and fairness respectively. The problem is formulated using bi-level convex-concave optimization, leading to sub-linear regret and cumulative fairness constraint violation bound.
Zhao et al. (2021a), an online meta-learning algorithm FFML has
Although fairness-awan aware meta learning is well studied concept, Our work differs from the above discussed existing works in several aspects. For example, Slack et al. (2020) integrates fairness regularizers into the MAML loss function. While the Fair-MAML, model discussed in their work might seem similar to ours, it focuses more on K-shot shot learning and does not focuses on individual task treatment based on subpopulations. In contrast Waan et al. (2022) uses a weighted loss function. Our approach stands out by its task specific inner loop optimization, the explicit consideration of subpopulation characteristics during the training process and absence of regularizers. The of binary cross entropy loss is distinctive to approach.
3 Preliminaries
3.1
MAML
Model Agnostic Machine Learning (MAML) Finn et al. (017)) is a popular strategy used in meta-learning. The strategy aims to learn a flexible initialization of a model that can be easily adapted to generalize to many downstream tasks with limited data. Therefore, it is very suitable for few-shot classification tasks. It works initializing the model parameters that a small number of gradient steps will help the model learn new task effectively.
At the core, MAML deals with the problem that machine learning models require large amounts of task- specific data to perform well. On the other hand, humans learn new skills and concepts quickly. For example, a person who has learned to drive a car will find driving other vehicles, such as trucks and buses casier. We also expect machine learning models to behave similarly and learn from a minimum amount of data. MAMI, avoids this problem by decomposing the model update process into two steps
Outer Loop Update (Meta-test task and Meta Update): The outer loop seeks to optimize the model parameters to adapt to new tasks quickly. It is done by finding suitable initialization of model parameters that perform well various tasks.
Inner Loop Update (Meta-train Task-Specific Update): It can be visualized having a task- specific learner. The inner loop will use the initialized parameters to adapt to specific tasks using only a few examples from the given task.
The set of data points used in the inner loop is referred to as the support set (X,), and the set of data points used in the outer loop is called the query set (X,). In the inner loop, the loss is computed over task-specific data. This shows how well the model performs on a task using current parameters. The model parameters are then updated using the gradient descent algorithm (i.c., the backpropagation technique in the case of neural networks). This may be performed on set of tasks, and in the outer loop, the losses across all the tasks are aggregated, and the model parameters are updated using gradient descent. The outer loop's ability
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to initialize the parameters and the inner loop's ability to fine-tune them makes MAML suitable for wide range of tasks where generalization and adaption diverse set of tasks are important.
In supervised learning, the meta model, parameterized as fa, is trained across tasks T = {x, L} with dataset X and loss function (C). Optimal parameters 0r are found by minimizing the following expected loss:
f- = arg min By Prr
(1)
where P(T) is the distribution over the tasks.
Few-shot meta-learning involves an inner loop for fine-tuning on the support set X;:
0' = - -Voiooo
(2)
where 7j is the learning rate.
The outer loop optimizes 0 for improved generalization:
arg min EI (eo
(3)
This approach harnesses multiple lasks to adapt 0, enabling effective generalization to new lasks with limited labeled data. Figure [ provides brief overview of the MAML strategy. The green region denotes the inner loop updates on the support set (X.). The purple region denotes the outer loop update over the query set essence, MAML aligns the updates computed during the inner loop on X,
(X₂) that acts as a meta-test. with those computed during the outer loop on Xg, enabling better generalization for the model parameters onto the (new) meta tasks (despite a small number of data samples).
Moreover, MAML learn various complex and challenging tasks by increasing the number of iterations (I, in Figure 1 in the inner loop. By performing multiple inner loop updates, the model can generalize better to a specific task and fine-tune its parameters on task-specific data. Another advantage of MAML is that it is a model and task-agnostic algorithm that can be applied to any learning model and algorithm trained using the gradient descent procedure.
3.2 Fairness Metrics
Various fairness metrics have been proposed to mcasure the bias and discrimination in machine learning models quantitatively. Two of the widely used metrics are demographic parity and equalized odds. We used the following metrics to assess the fairness of models trained in this work.
Demographic Parity (Statistical Parity): It measures whether the positive predictions in a model's output are equally distributed across all the demographic groups. model fair if it has roughly equal positive outcome rate across all the subpopulations. Formally model satisfies demographic parity if:
P( = 1/A = a) = p( = 1|A=b) = b)
where ÿ represents the model's prediction and A represents the sensitive attributes for all a, b bEA A
Equalised Odds: It is fairness metric that evaluates whether the true positive and false negative rates are equal across different demographic groups. In other words, it measures if the model treats all groups equally in correctly classifying positive instances (truc positives) and incorrectly classifying negative instances as positive (false negatives). Formally a model satisfies equalized odds if:
P( - i, i = a) p(Ý = 1|Y -i, i
where Y represents the model's prediction and A represents the sensitive attributes for all a, and i E (0, 1} the targets for binary classification task.
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4 Proposed Method
this section, we describe the methodology employed to improve fairness in the proposed approach. Our approach capitalizes on the unique characteristics of MAML to generalize better onto new tasks, while we employ Multi-Layer Perceptron (MLP) the underlying learning architecture. The Crux of the proposed approach is the update strategy of the MLP classifier. We divide the training dataset into two subpopulations: protected and unprotected. Because of the inherent bias present in the dataset, they can be treated as two distinct tasks. Via aligning these tasks, we observe that the classifier considers both important and learns to behave similarly towards both.
In MAML, during the inner loop, the model is fine-tuned on the support set, which comprises (multiple) task-specific data. Then in the outer loop, the model parameters are updated based on the losses computed the query set.
The objective of the MAMT-like methods looks as follows:
min Couter (arg min Linner (0o, Xa), Xa),
(4)
0g
On
where fg indicates the initial parameters, Cinner denotes the task-specific inner loss (meta-train). and Couter denotes the outer loss (meta-test), and X, and Xq denote the inner loop and the outer loop training example sets.
In approach to improve the fairness, focus single task in the inner loop of MAML. framework, executed on X,. The X, consists of all the protected samples and the X, consists of all the unprotected samples in the training data. Thus, the objective optimized by our approach is
min C(arg min L(oo, Xz), Xg)
(5)
8u
80
We perform single or multiple gradient steps based on the configuration (ref section 5.4) using samples from Then we optimize on the remaining batch samples with the parameters 0, and make final update. Here 0' represents the model parameters after the inner loop has finished i iterations.
the protected class, on parameters 6g obtain H¹ as - -1 oo--a - X.).
Note that the subpopulations are divided based on the sensitive feature attributes, and they will have instances of both labels, although skewed in number. The loss function (L) used in both inner and outer loops is binary cross-entropy loss. Note that the inner loop can be run for multiple iterations before executing the outer loop.
4.1 Gradient Alignment with meta-learning
Inspired by Patel et al. 2023) this subsection details how the MAML framework aligns the loss gradients computed on the two subpopulations.
One can rewrite equation as
min C(O', Xx) L(O0 V, X.),
(6)

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OC X4)
00' (chain rule of differentiation)
V
00u
00u
0(0- 1/VC00, Xx))
VC(O',
00.
FC((0"X,).(1 -10
(VL(00, X,)) V2 [(00, X²) 1/V (.))( n/V2 X.))
(Taylor series approx. of VL(0", X,) in the neighborhood of 0n)
~ VL(00, X₂) 7/V222 X,) VL(00. X.) - L (.)_,(,,.VVV/// VL)o. Xg)
(Given that 1j is small, higher terms involving 1/ are negligible)
VL(0o, X,,) n VC((.. X,) VL((0,
(using the product rule Va-b Vb V(a-b))
Observing the above gradient equation closely reveals that for reducing L(B', .X)). the overall gradients would minimize C(0,, X₂) while maximizing the product VL(0o, X.) VL(Oo, Xg). In other words, optimizing for equation (6] causes regularization effect to align VL((o, X,) and (L(, This avoids any sub- population strongly driving the learning process, reducing bias in the resulting model.
5 Experiments
5.1 Datasets
To evaluate the proposed framework empirically, we use the following four real-world datasets.
Compas Dataset ProPublica (2033): contains information about the criminal defendants who have been arrested from Broward Country, Florida. The dataset is used for learning models that predict the likelihood of re-offending based attributes such gender, race, prior criminal history, and risk scores. We consider binary sensitive feature whether a criminal is African American or not.
The Compas dataset has been widely used in research but is subject to criticism because of inherent biases in the data affecting minority groups. Many studies ( RRudin et al. (2020), Bao et al. 2022) raise concerns about the ethical implications of using this dataset. Despite these issues, Compas dataset remains relevant in fairness research because of it's widespread use. However, caution is advised when interpreting results from this dataset due to its known limitations.
Adult Dataset Becker & Kohavi (1996): contains 48, 842 instances, each characterized by 14 attributes. These attributes provide insights into the demographic and socioeconomic status of subjects such as age, education, occupation, and marital status. The dataset includes binary label that classifies an individual's income level into categories: above below $50, 000 per year. We consider gender, ie. male and female the sensitive feature attribute where females regarded as the protected subgroup.
3. Communities and Crime Dataset Redmond (011 aims to predict the crime rates using attributes of communitics in the United States including educational level, unemployment rates, poverly rates, and race. The predicted variable violent crime rale is a continuous value. To fa- cilitate our analysis, we convert it into a binary label based on whether the community is in the top 50% violent crime rate within the state Sllck et al. (2020). Additionally, We include a binary sensitive column that receives a protected label (1) African-American constitute the highest or second highest percentage of the community's racial makeup.
German Credit Dataset Hofmann (1994): contains data of 1000 loan applicants with each ap- plicant being classified "good" or 'bad' credit risk based various attributes such age, sex, job
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and credit history. To examine the potential biases in loan approval Sex is considered as sensitive attribute. Approximately 30% females in the dataset form the protected group and 70% males form the unprotected group.
The choice of these datasets was driven by their popularity among recent fairness literature and the valuable diversity they contribute, reflecting variations in social settings, sensitive features, and class imbalance. Despite their apparent similarities, these datasets differ in sensitive features ranging from race and gender to income levels. This diversity in sensitive features is critical for our evaluation, highlighting the framework's robustness across various real-world scenarios.
5.2 Baseline Models
To facilitate comparison, we establish bascline models for cach of the datasots using Multi-Layer Perceptron (MLP) with a single hidden layer. The baseline is construcled without the use of the MAML framework. A fixed learning rate of 0.001 is utilized, and the ReL.U activation function is applied during training. Tn Table 1 present a comprehensive overview of the accuracy and fairness metrics obtained from the baseline models across all four datasets. It is observed from the bascline model results (first row of cach horizontal panel in Table [ that the accuracy values are notably high. However, a closer look at fairness metries indicates the high values of Demographic Parity Difference (DP diff) and Equalized odds Difference (EOD Diff). This indicates the presence of bias in the model particularly towards the minority group and highlights the importance of addressing the fairness concerns.
MBA for DP and EOD:
We also compare the proposed algorithm with a manually adjusted base- line for Demographic Parity Difference ( (Kamiran & Calders (2011) ) and Equalized Odds Difference ( (Hardt et al. (refo) second and thirds rows of cach datasct in Table D.. For the mamual
adjustment of DP diff, equal representation of subpopulations in the training data is ensured using oversampling from the protected group. This is done to handle class imbalance, with the goal of improving fairness by ensuring that both protected and unprotected groups are equally represented during training. To manually adjust the baseline for better EOD, we ensure that the True Positive Rate (TPR) and False Positive Rate (FPR) are the same across the sensitive groups. Misclassified samples in each group are rebalanced to equalize these rates, making sure that the model's error rates do not disproportionately affect the fairness of one group more than the other.
Additionally, a threshold-based baseline was implemented to optimize demographic parity difference, where different classification thresholds ranging from 0.1 to 1.0, with a step size of 0.1, were applied for the protected group to further adjust and improve fairness metrics. The corresponding DP diff values and accuracies are plotted in | However, since the optimization is focused on DP diff, the Equalized Odds diff values are relatively poor. In the third row of each dataset in Table D we include the EOD Difference, accuracy, and DP Difference for the threshold that minimizes the DP Difference.
Threshold Adjustment:
While we present three manually adjusted baselines, it is important to note that multiple baselines could be constructed to optimize either DP diff, EOD diff, both, based the specific fairness objective being pursued.
5.3 Experimental Setup
The datasets at hand not only differ in terms of complexity but also in size and underlying nature of bias. Because of this, we use slightly different model architectures and hyper-parameters such as activation functions, and learning rates of the inner and outer loops across different datasets.
The datasets were divided into training, validation and test sets in a 70%, 15% 15% split respectively. We optimized the bascline models for accuracy and the selected hyperparameters were used for subscquent training of the models. We explored various combinations of inner and outer learning rates to identify the most, effective settings. During training and validation, the sensitive features were excluded from the model
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Table I: Comparative analysis of results obtained across the four datasets over independent runs, Results are presented varying the number of inner loop iterations for both the proposed configurations. Arrows (1. 4) next to the metrics indicate the direction of better performance (more is better and less is better, respectively). Bold values indicate the best performances. Please note that the threshold adjustment is primarily aimed at optimizing the DP difference, though in some cases it may also incidentally improve the EO difference. (Here Prot. represents protected samples and Unprot. represents uprotected samples and Manual Baseline Adjustment (MBA) refers to the manual tuning of fairness baselines)
No. of inner iterations
Accuracy (1)
DP diff (1)
EOD diff
Dataset
X, and X₃
Baseline Models
67.37
0.24
0.25
MBA for DP
67.86
0.26
0.26
MBA for FOD
63.67
0.16
0.17
Compas Dataset
Threshold adjustment
56.27
0.06
0.11
59.33 + 0.02
0.07 = 0.06
0.07 = 0.05
Config. 1
60.21
0.02
0.06
0.02
0.07
0.02
57.55
0.02
0.04
0.05
0.04
0.04
Config. 2
10
59.30 + 0.02
0.04 + 0.03
0.04 + 0.03
Baseline Models
82.86
0.21
0.30
MBA for DP
82.59
0.20
0.25
MBA for EOD
83.02
0.12
0.10
Adult Dataset
Threshold adjustment
80.19
0.05
0.34
67.05 + 0.07
0.08 + 0.05
0.11 = 0.05
Config.
10
52.58
0.07
0.1 + 0.06
0.07 = 0.04
64.58
0.06
0.07
0.07
0.07 + 0.12
Config, 2
10
60.57 + 0.08
0.09 + 0.04
0.12 + 0.05
Baseline Models
70.22
0.14
0.12
MBA for DP
73.58
0.16
0.10
Communities
MBA for FOD
70.50
0.13
0.10
& Crime
Threshold adjustment
49.
0.01
0.01
Dataset
59.80 + 0.03
0.06 + 0.04
0.06 = 0.03
Config.
63.25
0.06
0.07
0.08
0.08
0.11
58.59
0.03
0.06
0.06
0.07 = 0.05
Config. 2
10
62.00
+ 0.08
0.08 + 0.04
0.06 + 0.05
Baseline Models
71.47
0.15
0.18
MBA for DP
71.4
0.11
0.17
German Credit
MBA for EOD
72.40
0.11
0.15
Dataset
Threshold adjustment
70.0
0.01
0.07
70.70
0.04
0.04
0.04
0.04 = 0.03
Config.
70.00
0.05
0.03
0.01
0.07
0.02
70.67 + 0.01
0.03 + 0.03
0.04 + 0.03
Config. 2
10 71.00
0.01
0.02 0.01
0.04 0.02
to prevent any bias in the model learning. However, the sensitive features were incorporated during the inference phase to ensure that the performance metrics consider these critical attributes. For the compas dataset, an MLP with one hidden layer, the ReLU activation function, and inner and outer learning rates of 0.01 were used. For the Adult dataset, also similar setup with two hidden layers was used, and the inner and outer loop learning rates were 0.001. For the Communities and Crime dataset, MLP with one hidden layer, tanh activation function, and inner and outer loop learning rates 0.005 and 0.001 were used. For the German Credit Datasot, MLP with one hidden layer, relu activation function and inner and outer Icarning rates of 0.005 and 0.1 were used. We use the fairlearn library Weerts et al. (023) compute the mentioned fairness metrics.

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Table 2: Comparative analysis results obtained from all four datasets averaged over five independent runs, exploring the impact of different proportions of protected and unprotected samples within the support set (X₂) and the query set (X₂)
Dataset
Num. of inner iter.
Xy X.
Accuracy (t)
DP diff (4)
EOd diff (4)
64.45 + 0.04
0.16 = 0.09
0.17 0.10
70 30
63.80 = 0.04
0.16 = 0.09
0.17 = 0.09
59.69 + 0.03
0.07 1 0.06
0.08 0.06
50 50
59.69 1 0.03
0.05 1 0.03
0.05 1 0.02
compas Dataset
80 20
59.88 = 0.08
0.06 = 0.04
0.06 = 0.04
70 30
61.02 + 0.04
0.08 = 0.06
0.08 = 0.06
10
60 40
59.51 + 0.03
0.05 + 0.03
0.04 + 0.02
50 50
59.43 + 0.02
0.04 = 0.02
0.04 = 0.02
80 20
75.53 + 0.01
0.08 + 0.07
0.08 + 0.07
70. 30
70.75 = 0.05
0.07 = 0.05
0.09 = 0.05
0.07 64.58
0.07 = 0.05
0.07 0.05
50 50
Adult Dataset
:
63.67
0.09
0.13 1 0.08
0.13
0.09
70 30
52.89 + 0.11
0.10 = 0.07
0.07 = 0.05
10
40
52.58 + 0.07
0.10 - 0.06
0.07 = 0.04
50 50
80 20
62.45 + 0.02
0.08 + 0.05
0.09 0.01
70 30
63.10 + 0.02
0.1 + 0.04
0.07 + 0.04
56.94 = 0.03
0.05 = 0.05
0.03
Communities and crime Dataset
50 50
59.80 = 0.03
0.06 = 0.04
0.06 = 0.03
53.78 1 0.03
0.05 1 0.04
0.05
70 30
52.78 1 0.03
0.09 1 0.03
0.12 1 0.07
10
52.93
0.06
0.07 1 0.08
0.10 0.011
50 50
52.83 + 0.06
0.06 - 0.08
0.07 = 0.09
80 20
71.29 + 0.04
0.04 + 0.04
0.05 + 0.05
70 : 30
69.57 + 0.05
0.04 + 0.04
0.09 + 0.07
60 40
70.70 + 0.04
0.04 + 0.04
0.04 0.03
German Credit Dataset
50 50
69.17 + 0.03
0.03 = 0.02
0.07 0.04
70 30
70.70 = 0.04
0.03 = 0.02
0.06 = 0.05
10
70.00 1 0.05
0.03 1 0.01
0.07 0.02
50 50
The experiments were conducted on workstation with an Intel(R) Core(TM) 17-9750H CPU running 2. 2.60GHz, which features physical cores and 12 logical threads. Additionally, the system equipped with Windows 10 operating system. The software environment utilized Python version 3.11.1 for implementing and running the experiments. The longest training time required for the proposed method was less than 5 minutes, and the inforence time was instantancous.
NVIDIA Geffrce GTX 1650 GPU and 32 GB of RAM. The experiments were performed
5.4 Results and Analysis
We present a couple of variants (of the support and query sets) line with our proposed methodology. Note that the flexibility of the MAML framework to distribute tasks between the inner and outer loops can be exploited to accommodate different variants. However, we present the following two as very intuitive configurations.
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Table 3: Accuracy results for protected and unprotected groups on the compas dataset for varying numbers of inner iterations and different splits of X, and X,
Number of inner iterations
Protected
Unprotected Accuracy
Xq : Xs
Accuracy
Baseline model
0.6682
0.7081
80:20
0.6246
0.6706
70:30
0.623
0.6571
60:40
0.5871
0.6554
50:50
0.5922
0.6416
10
80:20
0.5767
0.6326
10
70:30
0.5904
0.6427
10
60:40
0.5788
0.6495
10
50:50
0.5888
0.6405
Diff Accuracy for Compass Dataset
DP Diff Accuracy Adult Dataset
Baseline
fa415r0d Caseline Aduntad
2.5 0.0
Baseline
Ra41orcd
Basa tra Aquieed
56
Accuracy thigher is better)
0.6
Accuracy (higher better)
110020
30:70:0
1:00.00
10:00.2
10:80-20
60
A15
1:500.50
60
36-
Q4
0.3
10:70-30
10:60-40
30
0.0
DP Diff [lower better)
DP Diff (lower better)
(a)
(b)
DP Diff Accuracy for CnC Dataset
DP Diff Accuracy for German Credit Dataset
ire
RatiGad
Baseline
Pabtora Biseline Adjusted
baseine aduustd
Accuracy (higher better)
Accuracy (higher is better)
65
1:70:30
1:80-20
ite
0.4
60
6.50-50
29-
.22
0.8
55
50
0b
DP Diff (lower is better)
DP Diff (lower is better)
(c)
(d)
Figure 2: Trade-off plots illustrating the relationship between accuracy and demographic parity difference across various datasets, comparing the proposed method with the baseline using adjusted threshold.
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1. Configuration 1 In the initial setup, we curate X3 with all the instances from the protected in the case of the compas dataset and female gender in the Adult
group (e.g., dataset). Correspondingly, X, is formulated with all the instances from the unprotected group. The This design facilitates the alignment of loss gradients for both the subpopulations, thus enabling the model to effectively counter the bias (refer to sec.
2. Configuration 2 : In the second setup, the configuration seeks counteract the way in which the baseline model learns the bias. For instance, in the compas datasct where label corresponds to the person gelling rearrested in the next two years, whereas label 0 indicates no rearrest in the subsequent years. From the predictions and fairness behavior of baseline models, it is evident that the minority group i.e. African-Amess Americans, are more likely to be predicted to re-offend as compared to other groups. They are more likely to be predicted positively than Caucasians. Hence, in X, we assemble all the instances of individuals from the protected group African-Americnn truth label 0 and instances of the unprotected group with ground truth label 1. Opposite in Xg, we keep all the instances of the protected group with ground label and the samples of the umprotected group with ground label 0. In this configuration, the alignment factor as the dot product between the gradients of query and support set (refer to sec. 4.1p is important because it ensures that the bias-reducing updates from the inner loop remain effective when the model is tested on a query set that would typically reinforce bias. It encourages the MAML framework to fine-tune its prediction contrary to prevailing bias.
with ground this,
Similar adaptations are made for the Communities and Crime dataset; protected samples with ground truth label and unprotected samples with ground truth label 1 comprise X,, whereas the protected samples with ground truth label and unprotected samples with ground truth label 0 are included in Xy. There is a slight modification in the Adult dataset. Since gender is chosen as the sensitive attribute, the male is the privileged group with a 31% probability of having a positive outcome (> $50K) compared to 11% probability of having positive outcome for the female group. Hence, we keep protected samples (Females) with ground truth and unprotected samples with ground truth 0 in the X,, Xg includes protected samples with ground truth label O and unprotected samples with ground truth label 1. Similar setup is used to design X, and X, in German Credit Dataset.
The results for both these configurations are presented in Table [ (rows 5 and 6 in each of the horizontal panels). Clearly, the results show substantial improvement in fairness metrics compared to the baseline models. For the compas dataset, Configuration 1 brings about a noteworthy improvement in the Demographic Parity Difference (DP Diff), reducing it from 0.24 in the bascline to 0.06. An even more impressive DP Diff of 0.04 achieved in Configuration 2. A parallel trend observed in the Equalized Odds Difference (EOD Diff), which undergoes a substantial decrease from 0.25 in the baseline to 0.07 in Configuration 1 and 0.04 in Configuration 2.
In the context of the Adult Dataset, the baseline DP Diff of 0.21 experiences substantial reduction to 0.08 in Configuration 1, further decreasing to 0.07 in Configuration 2. Simultancously, the EOD Diff' improves from 0.30 in baseline models to 0.07 in Configuration and Configuration 2.
The Communities and Crime dataset also shows similar trends, with the DP difference improving from 0.14 in the baseline model to 0.06 configuration and configuration 2. Correspondingly, the EOD Difference improves from 0.12 in the baseline model to 0.06 in configuration and configuration 2.
For the German Credit Dataset, the DP difference improves to minimum of 0.2 in configuration 2 as compared to 0.15 in the bascline. Similarly, the EOD difference also reduces from 0.18 in the bascline to 0.04 in configuration 2 with 10 inner iterations.
The results in Table ! show the effectiveness of both configurations in improving faireess across the four datasots. However, there is a decrease in accuracy. Substantial advancements in fairness metrics counter- balance this decrease. Importantly, these improvements are achieved without resorting to exclusive fairness regularizers (optimizing the fairness metries) any of the pre-processing techniques. This observation high-
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Table 4: Results on compas dataset for one inner loop iteration when the X, comprises unprotected samples
and X2 comprises protected samples.
Xg
X,
Accuracy (1)
DP diff (J)
EOD diff (1)
80
20
56.57
0.09
0.10
70 30
57.18
0.09
0.10
60 40
55.58
0.06
0.08
50 50
35.32
0.06
0.08
lights the inherent potential of the proposed gradient aligument approach in addressing bias and promoting fairness in machine learning models.
5.5 Varying proportions of X, and X₃
In light of the notable improvements achieved in enhancing fairness metrics through Configuration 1 and Configuration 2 (refer to sec. 5.4], addressing the trade-off between fairness and accuracy is crucial. While these configurations yield remarkable fairness improvements, there is a concurrent reduction accuracy. We undertake the following exploration to strike a balance between these two essential metrics.
Here, we opt for a distinct setup involving controlled split of the training data between the two subsets: X, and Xy. The idea here is to vary the ratio of protected samples within X,, while accommodating the remaining protected samples and all unprotected samples within Xg. This ratio manipulation occurs within the 80-20 to 50 - 50 range. This allows us to navigate the trade-off spectrum between fairness and accuracy and choose a desired point of operation.
However, it's imperative to note that the dataset's inherent composition constrains the ability to reduce the ratio beyond 50 50, as protected samples always constitute less than 50% of the total data. This is especially pertinent in the Adult and German Credit dataset, where the limited number of protected samples precludes the achievement of 50 50 split.
Table 2 provides insights into the interplay between fairness and accuracy within different configurations. Remarkably, it is evident that the highest accuracy values are consistently associated with an 80 20 split between Xg and X,. This is bocause most of the training data lics in the outer loop in the form Xg: making the model prone to dominances similar the baseline models.
Moreover, the ablation study reveals a discernible pattern: an increase in the proportion of protected samples within X, corresponds to improvement in fairness metrics. Note that this trend may not be strictly linear due to the saturation level in the metrics values (leads to only small numerical differences after the enhancements). However, the general trajectory supports our hypothesis that strongly aligning the loss gradients between the subpopulations leads to a fairer classification model.
Based on the experimental outcomes, classífication on the compas dataset demonstrates peak accuracy of 64.45 at one end, along with an impressive Demographic Parity Difference (DP Diff) and Equalized Odds Difference (EOD Diff) of 0.04 at the other. For the Adult dataset, the operating range spans between peak accuracy of 75.53 and fairness metrics of 0.07 DP Diff and EOD Diff. Similarly, the highest accuracy observed for the Communities and Crime dataset is 63. 10, with the best DP Diff and an EOD Diff' of 0.05.
The results also show how increasing the number of inner iterations improves the model's fairness. These out- comes display significant enhancements fairness metrics compared to baseline models. Most significantly, the results from Table P unveil crucial strategic insight: by varying the ratio of X, X,, we can dynami- cally achieve desirable equilibriu between fairness and accuracy. This flexibility empowers operable point with the desired accuracy and fairness behavior without altering other parameearrmeters..
find an
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Table 5: Performance on real datasets. Metrics with indicate the larger the better, and + indicates the smaller the better. Best performances are labeled in bold. The empty entries indicate that the referenced did not work with the corresponding dataset. lataset.(EODR, refers to the Equalized Odds Ratio and DPR represents the Demographic Parity Ratio)
Communities
compas
Adult
and Crime
Acc.
DPR
EODR (t)
Acc. (t)
DPR (1)
EODR
Acc.
DPR
EODR
(1)
(t)
(t)
(1)
(t)
(f)
Fair-MAME
58
0.81
75.4
0.66
Slack et al. (2020)
m- Ho-FF FTML
67.91
0.54
0.43
48.69
0.38
0.29
Finn et al. (2019)
FFML
61.35
0.91
0.87
59.57
0.74
0.69
Zhao al. (2021a)
FaAlGrad
59.43
0.95
0.96
52.58
0.86
0.90
59.8
0.87
0.85
(ours)
5.6 Swapping the subpopulations
After demonstrating the effect of varying the proportion of protected samples in X,, we now discuss swapping the subpopulations within the Model-Agnostic Meta-Learning (MAML) framework. In other words, the number of unprotected samples X, was varied, and X, encompassed the remaining unprotected samples and all the protected samples. As the split of X, increases, more unprotected samples are kept in the inner loop, and a decrease in accuracy and an improvement in fairness metrics is observed. Table 4 contains the results after swapping the subpopulations for compas dataset.
This observed trend is consistent with the inferences from our prior experiments (refer to sec. 5.5 despite swapping protected and unprotected samples within the MAML framework. This observation can be at- tributed to the fundamental concept gradient alignment inherent to the proposed -nsspirdd training framework (refer sec. 4.1 This happens because of aligning of loss gradients the inner loop and outer loop in the MAML framework. The model tries learn the common features between X, and Xg. making the learning independent of the sensitive feature attributes. This separation of subpopulations i.e. protected and umprotected groups, helps the model to behave fairly via gradient alignment.
5.7 Comparative Results
The elfectiveness of our proposed approach is evaluated through a comprehensive comparison with other saate-of-theaat models and methodologies that employ the Meeaning framework to enhance fairness in machine learning models. The models for this comparison include:
Fair MAML (DP regularized) Slack ct al. 2020
m-FTML
Finn al.
2019
FFML
Zhao et al. (2021a)
Table presents the accuracy and fairness metrics for comparison. Upon careful evaluation of the results, the proposed method, FaAIGrad, demonstrates notable superiority across all four datascts compared to the aforementioneel mela-learning--rivee works for improving fairness. Particularly striking improvements are observed the context the Communities and Crime dataset. To facilitate a meaningful comparison, we have selected the west-performing split between X, and X, for each dataset from Table 2 This ensures fair and relevant performance assessment against existing approaches wherein EOD Ratio and DP Ratio have been used for comparison.
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5.8 Demonstrating Bias Reduction in FaAlGrad
In this subsection, we demonstrate the reduction in bias achieved by the proposed FaAlGrad algorithm using the feature attribution technique. Specifically, we use the SHAP Lundberg & Loc (2017) graphs to interpret the reduction in bias. SHAP values provide the contribution of each feature to the classifier's prediction. In the context of fairness, SHAP values will help find how different features (especially sensitive ones such as gender, race, etc.) affect the model prediction. SHAP graph visualizes the impact of individual features on the model's prediction for a specific data sample. A positive SHAP value indicates that the feature contributes to a higher prediction (favors the positive class), whereas a negative value indicates that the feature contributes to a lower prediction (favors the negative class). To assess the effectiveness of our algorithm, we generated SHAP graphs for both bascline models and models trained using the FaAIGrad algorithm on various datasets. We chose beeswarm plots to present the SHAP values visually. The SHAP graphs were generated during the inference phase, that is the only phase when sensitive attributes were passed to the model.
Figure 3a and Figure 3D represent the SHAP graphs for the baseline model and the model trained using FaAlGrad, respectively, for the compas dataset. The features are ranked from top to bottom by their mean absolute SHAP values in the graph. The points are distributed horizontally across the x-axis based on their SHAP values. One may specifically focus on the contribution of sensitive features 'race' on the model's prediction. One can observe that in Figurd3a| higher values of the sensitive feature (protected group) have negative SHAP values, and lower values of the sensitive feature (unprotected group) have positive SHAP values. This indicates that the protected group (African Americans) has a higher likelihood of re-offending. This reflects the bias in the model's prediction. However, Figure 3b demonstrates the impact of FaAIGrad. Notice the diffcrence in the scale of the x-axis. Higher values of the sensitive fcature now cluster closely to the y-axis equally on both sides of the graph. This shows that the sensitive feature 'race' now impacts positive and negative outcomes equally. This indicates a significant reduction of bias by the model. Therefore, analyzing the SHAP graphs provides more evidence (apart from the metries) for the bias reduction achieved by our FaAlGrad algorithm.
We extend this analysis to the Adult dataset illustrating the algorithm's 's consistent performance in mitigating the bias. Figure 4a and 4b are the SHAP graphs for the baseline and FaAIGrad algorithm trained on the Adult dataset, respectively. Here, the sensitive feature is gender, and one may focus primarily on its contribution to the model's prediction. We see that in Figure4a higher values of the sensitive feature (refers to the protected group, females) have negative SHAP values, and lower values of the sensitive feature (refers to the unprotected group; males here) have positive SHAP values. This indicates that protected group (females) have higher chances of being predicted as having salaries less than $50,000 per year as compared to unprotected group (males). This reflects the bias in the model's prediction. However in Figure 4b we notice that higher as well as lower values of the sensitive feature now cluster closely to the y-axis equally on both sides of the graph. This shows that the sensitive feature of gender now impacts positive and negative outcomes almost equally. This indicates that the model is no longer biased toward the protected group.
Also, for the baseline model, it is observed that sensitive feature gender is ranked at the top, indicating its significant impact the model output. After applying FaAlGrad the feature gender shifts to bottom demonstrating a change in feature's importance. This shift indicates a reduction in the influence of the sensitive feature on the model's prediction, implying fairness enhancement of the model. Similar results were obtained when analyzing the algorithm on the Communities and Crime dataset. However, due to the large number of input features, we are unable to present the results here.
6 Conclusion
In this paper, we presented a meta-learning inspired training framework for alleviating the bias in classifiers. The core idea of the approach is to handle the dominance caused by certain subpopulations in the training data. The proposed approach demonstrates the ability of the MAML framework to naturally facilitate the
computation of loss gradients subpopulation-wiss and align them. To the best of our knowledge, this is not
reckoned by any of the existing meta-learning-driven works Finn et al. (2019); Slack et al. (2020): Zhao et al.
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priors_comue count
19h
age
length of stay
longth stay
Texture valuee
age
Fealuue valas
chargeo degree_
face
c_charge_degae degree_
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C. C_targe_degga degree M
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SHAP value (impact model output)
(a) SHAP Graph for baseline model Compas
(impact outputt
Dataset
(b) SHAP Graph for FaAlGrad on Compas Dataset
Figure 3: Comparison of SHAP graphs for different models on the compas Dataset
High
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age
hours per, workclass Private
marital status Single
hoursper__eek
occupation
education, HS-grad
education Some-college
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education,
education Some-colagee
marital status, Divarced
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Peeibre vahue
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education, School
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education, Masters
accupation, Otherüükkownn
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occupation_ Professional
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education Masters
workdass Self-Employed
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-1.00 -0.75 -0.3
-0.25
0.00
0.25 0.50
-20
-1.5
-1.0
-0.5
3.0
3.5
1.0
SHAP value jimpact on modil nutput)
SHAP be (impact outpur)
(a) SHAP Graph for baseline model Adult Dataset
(b) SHAP Graph for FaAlGrad on Adult Dataset
Figure 4: Comparison of SHAP graphs for different models on the Adult Datasct
2021a) toward improving fairness. Moreover, the ablations reveal that the framework provides range of operating points to pick a desired system characteristic.
Without adding any exclusive fairness regularizers (in the form the discussed metries), optimizing only the cross-entropy loss significantly improved the fairness of the classifiers. We plan to investigate the behavior of the proposed 'gradient alignment' regularization in greater detail, particularly by adding more direct fairness regularizers, extending to more complex tasks such as vision datascts, realizing it outside the meta-learning framework, etc. Further, in contrast to many other approaches that heavily rely on preprocessing, our proposed method eliminates the need for any preprocessing.
It's important note that although the experiments may compromise on accuracy, this work represents a novel approach towards bias reduction by leveraging multiple tasks within MAML. The findings demonstrate considerable potential for leveraging these tasks to implement bias reduction effectively.
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Acknowledgment
We sincerely thank Dr. Manisha Padala, IIT Gandhinagar for her valuable discussions during experi- mental selup, which greatly contributed to shaping this work.
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Published in Transactions on Machine Learning Research (01/2025)
Chen Zhao, Feng Chen, and Bhavani Thuraisingham. Fairness-aware online meta-learning, 2021a.
Chen Zhao, Feng Chen, Zhuoyi Wang, and Latifur Khan. primal-dual subgradient approachfor fair meta learning, 2021b.
Indre Zliobaite. A survey on measuring indirect discrimination in machine learning, 2015.
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Reviews:
[{"review_id": "2HTaBrDtdu", "reviewer": "Reviewer_Er9X", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The paper tackles the problem of Responsible A.I. for ML models, which is an important issue\n- The idea of aligning gradients across subpopulations is sufficiently innovative\n- The results prove the benefits in fairness of the proposed approach compared to other baselines, with a reasonable trade-off in accuracy\n\nWeaknesses: \n- The number of dataset for Responsible AI benchmarking is limited, and this is a well known issue. I suggest to add at least all the common ones, adding “German Credit Dataset”. Moreover, see the section “Broader impact concerns” for further details on datasets.\n\nMinors/Typos:\n- Use one between subpopulations or sub-populations, not a mix of the two. I suggest “subpopulations”.\n- Page 9: in hand -> at hand\n- Page 11: Differnce -> Difference", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a new approach to mitigating bias in machine learning models by using a gradient-alignment strategy based on the Model-Agnostic Meta-Learning (MAML) framework. By aligning loss gradients computed across protected and unprotected populations, the paper proposes a method to regularize the training process for fairness without using any specific fairness regularizers. The experiments are conducted on multiple commonly used benchmark datasets (COMPAS, Adult, and Communities and Crime) and they show improvements in fairness metrics like demographic parity and equalized odds, with the usual trade-offs in accuracy. The core contribution of the work is the integration of fairness considerations directly into the learning process (in/meta-process), as opposed to pre/post-processing techniques.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Adding a paragraph discussing why COMPAS dataset is used, and warning about the potential issues of it (see Broader impact concerns)\n- Considering COMPAS issues, adding another dataset might beneficial for the paper, such as the “German Credit Dataset”", "broader_impact_concerns": "COMPAS dataset  has been discussed several times to be, at least, controversial for benchmarking usage for Responsible AI methods. See “It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks” (https://arxiv.org/abs/2106.05498), or “The Age of Secrecy and Unfairness in Recidivism Prediction“ (https://hdsr.mitpress.mit.edu/pub/7z10o269/release/7)", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "6FykibjoIo", "reviewer": "Reviewer_Edus", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths.\n- The paper is generally well written and easy to follow.\n- The main idea is simple and seems to work well.\n- There is a fair amount of reasonable ablations/experiments included to illustrate the method.\n\nWeaknesses.\n- Some details are missing (see below)\n- Comparison with other methods is limited. The possible trade-offs between accuracy and fairness is not clearly shown and compared with baselines and other methods.\n- Claims regarding the methods superior performance is not sufficiently supported by evidence.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Inspired by a meta learning framework (MAML), an inner/outer loop training strategy is proposed in which losses related to protected and unprotected subpopulations are optimized. It is demonstrated that the combined loss can be interpreted as a sum of the loss for the unprotected group and a gradient dissimilarity penalty to the gradient of the protected subgroup loss. Experimental validation on three datasets show that this training strategy leads to lower overall accuracy but strong improvements on two fairness criteria. Several ablations / configurations are included to demonstrate the method. Overall performance is compared with three competing methods.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Requested changes\n\n 1. Please provide a clear description of the training algorithm. Is it correctly understood that you alternate between one gradient step on Xq and 1 / 10 gradient steps on Xs?\n 2. Please write clearly in the datasets section what is considered the protected subgroup in each case (e.g. in adult, is it men or women?).\n 3. Please provide details regarding how the data was split into training and test.\n 4. Please include accuracies for all models seperately the protected and the unprotected group (e.g. in table 1).\n 5. Please provide details regarding how hyperparameters were chosen, e.g. did you optimize the baseline for accuracy and then reuse the same parameters for the remaining experiments?\n 6. Please also provide results for the baseline when doing a manual adjustment (on training or validation data) for demographic parity and equalized odds, to demonstrate whether the proposed method surpasses this baseline in accuracy.\n 7. I believe there should be a reference to table 3 in section 5.6.\n 8. Please provide comparison with other relevant competing methods on all three datasets. Since all results must be interpreted as a trade-off between accuracy and fairness criteria, I strongly recommend to show curves that demonstrate trade-offs that can be achieved with the different methods. For example, how can we interpret m-FTML vs. FaAlGrad on Adult when the former has better accuracy but worse DPRE and EODR? Showing the entire trade-off curve will provide a much better grounds for comparing the different methods.\n 9. \"Upon careful evaluation of the results, the proposed method, FaAlGrad, demonstrates notable superiority across all three datasets compared to the aforementioned meta-learning-driven works for improving fairness.\" I think this is an overstated claim. While results on Communities and Crime is superior, results on Compass and Adult are not evidently better.\n\nOpen questions and minor comments\n\n10. It would benefit the paper with a more elaborated discussion of different (conflicting) definitions of bias/fairness.\n11. The approach is based on the concept of a protected subpopulation, rather than a protected class, i.e., there is a built-in asymmetry. I would have liked more dicussion of this design choice.\n12. Related works section is difficult to read if unfamiliar with the cited work. It is focused on technical differences and does not give much intuition/overview of the conceptual differences between the different approaches.\n13. Given that this approach is limited to a single protected subpopulation, can it still be considered meta-learning? It seems to be more similar to a weighted learning scheme?\n14. In eq. 6 there is a further restriction to a single gradient update, but in practice you can use more that one gradient update in the inner loop. How does this affect the interpretation of gradient alignment?\n15. \"This approach Agarwal et al. (2018) framed the challenge of achieving accurate classifiers under fairness constraints as a two-player zero-sum game.\" Minor grammar issue.\n16. \"Along similar lines, the work of Slack et al. Slack et al. (2020) delves...\" Author name appears twice\n17. In the notation starting with Equation 1, would it be clearer to use subscripts like L_T and X_T to specify the loss and data associated with a particular task?\n18. The description of MAML could be a bit more concrete. For example, equation 3 suggests the outer loop optimizes an expectation, but figure 1 suggests optimizing only a single inner task.\n19. The role of the query data is not discussed in much detail, other than that it \"acts as a meta-test\". What does that mean in practice?\n20. Consider removing line breaks in the derivations below eq. 6 for improved readability.\n21. Consider writing explicitly what approximation is made in the second to last line in the derivation on page 6.\n22. Is there an approximation made in the last line in the derivation on page 6, or should that be an equals sign?\n23. Consider dropping vertical lines from table 1 for improved readability, e.g. use the booktabs latex package.\n24. Configuration 2 is not clearly motivated, and only discussed in the results section. Could you provide a more clear argument for this configuration, possibly already in the methods section?\n25. Consider just writing \"Config. 1\" and \"Config. 2\" in the second column of table 1 in stead of the longer descriptions of Xs and Xq which are clearly described in section 5.4.\n26. Consider also showing the results in table 2 as a graph. Consider including the baseline in table 2 as well as in the graph. Consider to include a 90:10 split also.\n27. In the experiment reported in table 2, consider leaving out the 10 inner iterations setting, since this experiment is seems to be more focused on reducing \"weight\" on the protected subpopulation which is countered by a high number of inner iterations.\n28. Is there any particular reason for using different fairness metrics in table 4 (DPR and EODR vs. DP Diff and EOD Diff)?", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "c3I5TGlmw7", "reviewer": "Reviewer_4uSp", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The main strength of this paper is the claim that some improvement on fairness metrics can fall out of a MAML formulation/gradient alignment - I think this is interesting and worth exploring. However, I have some doubts about the overall content of the paper and think this exploration needs to be clarified and pushed further to be a truly compelling paper.\n\nMy main critiques:\n- My main issue with this paper is that it’s not clear why I would use this method over one of the many other ones in the fairness literature that improves these metrics. It’s not clear that there are any distinctive features of this approach that either improves results (either generally, or in some subset of cases) or allows for broader applicability\n- On the flip side, I do think that “gradient alignment contributes to fairness metrics improvements” is an interesting conceptual observation, but there is not much work done in this paper to shed light on that insight and so I’m left not really knowing what to take away\n- Empirically, the takeaway is a little unclear - for instance, in Table 1, I don’t know if the accuracy hit should be considered “worth it” for the gain in DP. Usually this is resolved by showing a tradeoff between these two metrics along some sort of hyperparameter range, however I’m not sure the MAML approach has such a hyperparameter. All this to say that I think the empirical case could be much more compelling if some argument was made to help me compare the method to the baseline more cleanly\n- The SHAP plots seem to suggest that the sensitive attributes (race for COMPAS, gender for Adult) are used - is this true for all experiments? If so, is that required for this method to work? I think it’s non standard for these dataset to include the group labels as model inputs.\n\nSmaller points:\n- 2nd paragraph: It’s not clear to me what it means that these methods “treat fairness as an isolated concern rather than a core aspect of the learning process” - I don’t understand the distinction being drawn here\n- Intro: distinction btw dataset and feature bias is not clear - it seems like they are different categories of biases (dataset bias can be the root cause of feature bias)\n- Sec 4.1, Eq 6 - should clarify what \\theta^i is, I don’t think it’s defined explicitly\n- Table 4: should clarify why there are so many empty cells\n- There’s a claim at the end of 5.5 that increasing the number of iterations improves model fairness. However, in Table 2 this only seems to be true on the COMPAS dataset. For instance, on Adult, if we look at 60:40, we have iter=1 -> Acc=64, DP=0.07, EO=0.07, and iter=10 -> Acc 52, DP 0.1, EO=0.07. This is strictly worse at 10 iterations than at 1", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper discusses the use of MAML in a fairness setting, with one sensitive attribute corresponding to the support set and the other to the query set. The authors argue that the gradient alignment induced by MAML is helpful for fairness objectives. Empirically, they demonstrate that this approach is able to improve fairness metrics on some standard datasets, at the cost of some accuracy, and demonstrate that the SHAP values from the model after this method is applied put less importance on sensitive features.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See critiques above for details. I think the main thing for me is that there are two potentially successful versions of this paper: 1. makes a compelling argument for why this method should be used to improve fairness results instead of other existing fairness approaches, and 2. provides insight into this gradient-alignment/fairness hypothesis. I think the current submission could be a basis for either of those papers but at the moment it doesn't currently execute on either: it isn't (1) since it doesn't draw a useful distinction between itself and other fairness approaches, and it isn't (2) since there isn't much exploration (either theoretically or empirically) about why the gradient-alignment/fairness relationship might hold, and what's going on at a deeper level.", "broader_impact_concerns": "I generally advise against the use of the COMPAS dataset in fairness papers that do not explicitly consider the criminal justice context. See “It’s COMPASlicated” (Bao et al) for more details on this. I’d recommend at the very least a significant disclaimer about the issues involved in this dataset", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors addressed all the comments raised by the reviewers. In particular, they conducted additional experiments as requested and included additional material to clarify the open questions. Importantly, they also provided trade-off curves as requested by two out of the three reviewers. This was one of the key concerns. The authors also provided code and documentation to reproduce the results.
Decision: Accept (poster)