Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact
Authors: Berkay Chakar
Amina Izbassar
Mina Janićijević
Jakub Tomaszewski
Venue: Accepted by TMLR ()

Abstract: This reproducibility study aims to evaluate the robustness of Equal Improvability (EI) - an effort-based framework for ensuring long-term fairness. To this end, we seek to analyze the three proposed EI-ensuring regularization techniques, i.e. Covariance-based, KDE-based, and Loss-based EI. Our findings largely substantiate the initial assertions, demonstrating EI’s enhanced performance over Empirical Risk Minimization (ERM) techniques on various test datasets. Furthermore, while affirming the long-term effectiveness in fairness, the study also uncovers challenges in resilience to overfitting, particularly in highly complex models.
Building upon the original study, the experiments were extended to include a new dataset and multiple sensitive attributes. These additional tests further demonstrated the effec- tiveness of the EI approach, reinforcing its continued success. Our study highlights the importance of adaptable strategies in AI fairness, contributing to the ongoing discourse in this field of research.

Published in Transactions on Machine Learning Research (07/2024)
Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact
Berkay Chakar
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Amina Izbassar*
amina. iabassar @student. itsiian.dbusa uva.nd
Mina Janićijević"
minn..jeniicseexiiccsssdddee uva.
Jakub Tomaszewski"
jokub. hatp..ttoommssssssssdttddndnnttt
Reviewed on OpenReview:
https: /ttps://openreee opw.revee..eeeee..emeee net/ formm?? id= Yj8fU0GXXL
Abstract
This reproducibility study aims to evaluate the robustness of Equal Improvability (ET) an effort-based framework for ensuring long-term fairness. To this end, we seek to analyze the three proposed El-ensuring regularization techniques, i.e. Covariance-basee, KDE-based, and Loss-based EI. Our findings largely substantiate the initial assertions, demonstrating ET's enhanced performance over Empirical Risk Minimization (ERM) techniques on various lest datasets. Furthermore, while affirming the long-term effectiveness in fairness, the study also uncovers challenges in resilience to overfitting, particularly in highly complex models.
Building upon the original study, the experiments and multiple sensitive attributes. These additional tests further demonstrated the effec- liveness of the El approach, reinforcing its continued success. Our study highlights the importance of adaptable strategies in AT fairness, contributing to the ongoing discourse in this field of research. Our code is publicly available on GitHub
extended to include new cltaaset
1 Introduction
Throughout the last ten years, artificial intelligence (AT) has become an inseparable part of people's daily lives, penetrating every aspect of it. By being incorporated in myriads of applications not only is this tech- nology having rapidly increasing impact on society, but also the way people make life-changing decisions. However, it's been noted that these methods often display biases related to ethnicity, race, and gender (Casil-) las 2023 Angwin 2016). Therefore, while machine learning methods often benefit work efficiency and may help aulomate numerous processes, this inherent bias AI has sparked concerns about discrimination and the need for equity.
To account for the potential biases, numerous fairness-ensuring methods have been proposed over the recent years. Some of the most prominent ones are demoggaphicc parity (DP), equal opportunity (EO), and equal- ized odds (EODD) (Dwork et al. 2011 Hardt et al. 2016). The general idea behind the aforementioned metrics is that they measure if the probabilities across different demographic groups are equal for each class. Nonetheless, while the majority of existing fairness metrics prove to be successful in immediate fairness, i.e. measured statically, only using the predicted class labels, they struggle with ensuring in the long run. The notion of long-term fairness relates to situation in which an individual puts effort into improving their features in order become classified person increasing their income to be qualified for loan. While this shortcoming has already been identified in the literature c.g. Bounded Effort (BE) and Equal Recourse (ER), the existing methods have their limita- tions, namely vulnerability to imbalanced data or outliers for BE and ER respectively (Heidari el al. 2019
different class. A common example depicted in the literature is
TThe authors contributed equally this work.

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Published in Transactions on Machine Learning Research (07/2024)
Gupta et al. 2019 Motivated by these substantial issues, Guldogan al. propose Equal Improvability (EI) an in-processing effort-based fairness technique that does not suffer from those limitations (Guldogan et al. 2023). Furthermore, the authors claim that El ensures identical feature distributions for different groups in the long run and outperforms other methods ensuring long-term fairness. To prove the robustness and reliability of ET they conduct various experiments and present their findings.
In this study, we aim assess the reproducibility of the paper by replicating the experiments and further exploring various settings and scenarios to substantiate the robustness of the introduced EI.
2 Scope of reproducibility
The paper by Guldogan et. al. (2023) investigates the issue of fairness in AI. Specifically, addresses group fairness and proposes new method of ensuring long-term fairness called Equal Improvability(EI) Long- term fairness is concept in decision-making and policy that prioritizes equitable outcomes and opportunities over an extended period, rather than focusing on immediate or short-term equality. It involves considering future generations and the long-term impacts of decisions to ensure fairness and justice for all over time. In addition, group fairness a setting where different groups are treated statistically equally. The authors introduce El as a method for negatively classified samples to achieve an equal probability of being positively classified after improvement, regardless of which group the sample belongs In this study, we will verify the following claims of the original paper:
Claim 1: A classifier obtained by each of the three proposed EI ensuring methods, i.c. Covariance- based, KDE-based, and Loss-based ET (See section 3.1 has a significantly smaller El disparity value than the ERM (Empirical Risk Minimization) approach and a comparable error.
Claim 2: Most existing methods have an adverse effect on long-term fairness, while EI continues to enhance it. In other words, EI accelerates the process of mitigating long-term unfairness under the dynamic scenario where the data distribution and the classifier for each demographic group evolve over multiple rounds.
Claim 3: The introduced methods of achieving Equal Improvability, i.e. Covariance-based, KDE- based, and Loss-based EI, prevent an over-parametrized classifier from overfitting the data.
In the following sections of the study, we present the notion of Equal Improvability and outline the datasets, models, and hyperparameters used for evaluating its performance. In section 4 we seek to reproduce the results obtained by the authors by conducting experiments from the original paper. Moreover, we investigate the robustness of EI further by analyzing it in various scenarios and settings. Ultimately the final section of this study discusses the obtained results and evaluates the extent to which the initial claims proposed by the authors are substantiated.
3 Methodology
The official GitHub repository, shared by the paper authors, was found to be incomplete, encompassing code for conducting experiments only on Synthetic dataset. Nonetheless, the initially missing code was provided by the authors upon our request. We adjusted their environmental setup to ensure compatibility with our local machines. By employing the supplied experimental settings, we have managed to replicate the majority of results illustrated in the paper. In pursuit of exploring the gencralizability of the proposed method, we extend the original code and conduct additional experiments.
3.1 Model descriptions
Equal Improvability (EI) - is introduced by the authors as a novel concept effort-based fairness that aims to balance the likelihood of acceptance for previously rejected samples across various groups, under the assumption that each rejected individual will expend limited, specified amount of effort to improve
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their chances of acceptance (Guldogan al. 2023 It differs from other fairness metrics by focusing
the potential for improvement and equalizing acceptance rates for individuals who were initially rejected,
across different demographic groups. Refer to Appendix A for the equation of El. In addition, the authors proposed the following equation for solving the fairnes--roullarized optimization problem:
min feJ
(1 -
Σ E(u, ((x,)) ) AUs
(1)
They consider ways of defining the penalty term Us:
Covariance-based EI penalty: This method, inspired by Zafar et al. (2017). assesses the fairness of a score-based classifier f by measuring the covariance between the sensitive attribute and the maximally improved score of rejected samples within defined effort budget. The unfairness of classifier ʃ is quantified by (Cov(z; max Arlz/(< + (x)) < 0.5))², where I_ is the set of unqualified samples, and Az is the average sensitive attribute in this set.
2. KDE-based Method for EI: This approach, inspired by Cho et al. 2020 uses a Kernel Density Estimator (KDE) to approximate the probability density function of the score f(x). The KDE estimates the density the maximum achievable score by improving features within the budget A, and the EI penalty is computed by summing the absolute difference of probability values for cach group.
3. Loss-based Method for EI: This method calculates the fairness violation by computing the absolute difference of group-specific losses. The EI loss for group is defined as L, 1 DiEL: f-mmaxx delikf(zitde)) This loss measures how far rejected samples in group are from being accepted after feature improvement within the budget A.
Models Used for Testing: The majority of the tests were conducted using Logistic Regression (LogReg). Additionally, some tests were performed on Multilayer Perceptrons (MLPs), including:
A single-layer ReLU neural network with four hidden neurons.
five-layer ReLU network with 200 hidden neurons per layer.
These models were employed to validate the effectiveness of the different EI methods in ensuring fairness in decision-making processes.
3.2 Datasets
To assess the reliability of the feature and example importance methods, the authors developed one synthetic dataset and also employed two real-world datasets, specifically the German Statlog Credit and ACSIncome- CA (Dua & Graff 2017: Ding et al. 2021). Our study broadened this analysis by incorporating the Default of Credit Card Clients Datasct (DCC Datasct), which was selected due to its inherent gender and age bias, providing relevant. case for testing fairness in models (Yeh) 20166.. Such biased dataset allows us to evaluate the effectiveness of our methods in handling and mitigating biases. The class distribution, detailing this bias, can be found in Appendix [
Table provides a summary of the key details of these datasets. All datasets used for the experiments were split into training/ test sets in the ratio of 4:1.
3.3 Hyperparameters
The authors of the original study provided detailed hyperparameter configurations in Appendix C.2 of their paper, as well as within the supplementary notebooks. We adhere to these specified hyperparameters for our replication efforts, ensuring consistency with the original experiments. When conducting additional experiments, utilize similar hyperparameter settings to ensure that our results are comparable.
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Datasets Synthetic
Samples Classes
Num. of sensitive attrs.
Description
20,000
Synthetic data with improvable features and binary outcomes from Bernoulii/aussian Gaussian models.
German Statlog Credit
1,000
2
1 & 2
Credit risk data from Germany, assessing credit
ACSIncome-CA
195,665
2
1 & 2
Demographic and income data from California's ACS.
Default of Credit Card Clients
30,000
2
1
Taiwanese credit card client data, focusing on default payments.
Table 1: Overview of the datasets used for validating the proposed methods in the label-free setting.
3.4
Experimental setup and code
To replicate the experiments detailed in the original paper we leverage the official codebase provided by the authors GitHub] comprising series of Python scripts. However, the repository contained only the notebooks for reproducing experiments on the Synthetic Dataset, they were not compatible with both the German Statlog and ACSIncome-C, CA datascts due to discrepancies in hyperparamcter values. Consoquently, our leam needed to obtain the appropriate settings by directly reaching out to the authors. Moreover, we adapted the code to support GPU computations, ultimately resulting in substantial reduction training time. Further altorations were made in order evaluate the performance of EI in the scenario of multiple sensitive features. All the aforementioned modifications were implemented in Python with the codebase hosted Gil.Hmb
3.5 Computational requirements
Due time constraints and the need process four datasets of differing sizes, including the notably large ACSIncome-CA dataset with approximately 200,000 samples, our team conducted the experiments concurrently using both GPU and a CPU. Specifically, employed an NVIDIA T4 GPU and an Apple M1 Pro CPU chip for this purpose. Details regarding the runtimes and estimated energy usage are illustrated in Table 10. It should be noted that the estimated energy consumption figures are provided solely for the experiments conducted on the NVIDIA T4 GPU, consuming approximately 33Wh, i.c. 0.033kWh.
4 Results
In this section, we validate the main claims by replicating the experiments presented in the paper and extending them with a new experimental setup, including a new dataset and the integration additional sensitive features. Furthermore, we check whether Equal Improvability (EI) vulnerable to overfitting. The outcomes of our replicated experiments overall follow the same trends as the ones produced in the original paper.
4.1 Results reproducing original paper
EI Disparity and Loss value check: To verify claim 1, we compared how the 3 models, proposed by the authors, perform compared to ERM in terms of loss and El disparity. To accomplish this we reproduced both Table and Figure of the original paper in which Logistic Regression (LogReg)
used as the backbone model (see the obtained Table and Figure] T (Guldogan et al. 2023 Moreover, in Table t we reproduced Table 3 of the original paper containing the results obtained by training a simple MLP model comprised of a single hidden layer with 4 hidden neurons (Guldogan et al. 2023). Despite identifying fractional numerical differences (see Appendix B.3 for comprehensive details), the results the mentioned tables and figures were in general aligned with the ones illustrated the paper. Subsoquent scrutiny of these numerical deviations did not yield any solid conclusion. We suspect that the authors may have employed different seeds hyperparameters in their experimental setup, which has not been reported either in the formal report in
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the official codebase. Alternatively, the possibility exists that certain values were reported incorrectly. We eliminated the possibility of code differences in the core algorithm as the missing notebooks were obtained directly from the authors.
Dataset
Metric
ERM
Covariance- Based
KDE-
Loss- Bassd
Synthetic
Error Rate
.222 .002
.253 1 .003
.253
.009
.246 .002
EI Disp.
.118 .007
.003 + .002
.003 + .003
.002 .001
German Stat.
Error Rate
.220
.009
.262 + .009
.249
.031
.237
.008
EI Disp.
.041 + .008
.022 = .023
.226 + .388
.016 + .013
ACSIncome-CA
Error Rate
.185 .000
.200 + .000
.196 .001
.195
.000
El Disp. .031 .001
.008 + .001
.005 + .001
.006 .001
Table 2: Error rate and EI disparities of ERM and the three proposed EI-regularized methods on Logistic Regression (LogReg). The results indicate that El-based classifiers have lower El disparity without causing significant increase in the error rate, illustrating their reduced bias towards scnsitive groups.
Syuthetie
Gemman
ACSIncome-CA CA
Goottniesbed RDRbsee Low Bood
0.30
Coweareetaoo khi-l Low Baod
Ervor Rate
0.24
Ereor Rate
Error Rate
0.20
KDE KDE-based
Covattrncn
Optiaal
0.25
Lave Rase
0.19
0.22
0,20
0.00
0,05
0,10
0,00
0.05
0.00
0.02 E Disparity
EI Disparity
C1 Dispocity
Figure 1: Tradeoff bet ween El disparity and error rale for Logistic Regression (LogReg). All three introduced methods, Le. Covariance-basee Kasebased, and bass- EI, successfully find a tradeoff between the error rate and EI disparity, being the bottom left corner of the figure.
Yet, after a thorough examination of the obtained values we have concluded that our results substantiate claim 1, as models trained using the three introduced methods, i.c. Covariance-basse KDE- KDE-based, and Loss-based FI, perform better than the ERM all test datasets. Since the KDF-bbaeed method is only underperforming on one of the datasets, concluded that the authors might have used different hyperpa- rameters for this experiment, which is why we could not reproduce their results.
To assess claim 2, we replicated the experiments for Figure 4 by Guldogan et al. 2023) which illustrated the persistent unfairness over time for various algorithms. Our replicated figure indicates that the El classifier's long-term unfairness diminishes more rapidly compared to other fairness approaches, corroborating the authors' second claim. Additionally, we recreated Figure 5, observing the progression in feature distribution across three rounds of applying each algorithm. Notably, the EI method displayed narrower gap in feature distribution between groups than other fairness concepts (DP, BE, ER, and ILFCR). Our findings for both figures align perfectly with those originally published, confirming the accuracy of our replication. This consistency strongly supports the claim regarding the El classifier's long-term fairness effectiveness. The replicated figures can be found in Appendix B (see Figure 5)
Long-term (un)sairness check:
Robustness Check Against Overfitting: To verify the third claim, we replicated Table from Guldogan et al. (2023), which compared the Error rate and El disparities for both the ERM baseline approach and the proposed EI ensuring methods, on overparameterizee neural network applied to the German Statlog Credit dataset. The results of our replication are illustrated in Table 3 Although the obtained EI disparities were found to be aligned with the original values, showing either exact matches or differences within a margin of one-tenth, a notable variance was observed in the error rates. Closer inspection revealed a uniform discrepancy of 0.1 across all error rates, suggesting a potential miscalculation in the originally reported values. However, it was identified that none of the methodologics, including ERM, exhibited overfitting. Consequently, the premise that EI resistent overfitting is rendered moot, given the baseline method (FRM) itself shows no signs of overfitting. Therefore, we deduce that the third claim lacks validity.
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Metric
FRR.M
Covariance- Based
KDF- Based
Toss- Based
Train Error
.218 + -004
.233 + .003
.226 + .009
.233 + .012
Test Error
.218 + .010
.218 + .010
.222 + .007
.231 + .007
Train ET Disp.
.024 + .017
.018 + .011
.018 + .012
.009 + .009
Test El Disp.
.064 + .036
.050 + .024
.070 + .050
.062 + .015
Table 3: Error rate and ET disparities of ERM and the three proposed El-regularized methods on an overfit Multilayer perceptron (MLP).
4.2 Results beyond original paper
different dataset: While the reproducibility study proves the first. claim of the paper, we aim to scrutinize it further utilized in the original paper, i.e. German Statlog Credit and ACSIncome-CA, having considerable biases towards some of its sensitive groups (see Appendix D). To this end, we employ the Default of Credit Card Clients Dataset, containing a substantially lower bias for the Ser feature, as illustrated in Appendix D We trained the Logistic Regression model for 100 epochs, with a setting of single sensitive and improvable feature being Ser and Education respectively. Moreover, after tuning the hyperparameters we set the batch size 512 and the learning rate 0.01.
Evaluating EI
different dataset. That is due to the data in both datasets,
By analyzing the obtained results, depicted in Table 4 and Figure 6 indeed note smaller El Disparity for all proposed El ensuring methods, i.e. Covariance- KDF-based and Loss-based EI. Therefore, we did not observe any unusual divergences, we conclude that the experiment supports claim 1.
Metric
ERM
Covariance-Basee
KDF- Based
Loss- Based
Error Rate
.155 + .001
.177 + .001
.176 + .001
.176 + .001
EI Disp
.009 + .001
.004 - .002
.002 + .001
.003 = .001
DP
.020 .011
.017 + .001
.016 .001
.014 1 .001
EO
.027 .001
.018 1 .001
.016 .001
.009
.003
EODD
.027 - .001
.018 = .001
.016 = .001
.009 = .002
Table 4: Results of evaluating ERM and the proposed El ensuring methods on the Default of Credit Card Dataset using Logistic Regression (LogReg). The results indicate that El-based classifiers have lower EI disparity without causing significant increase in the error rate, illustrating their reduced bias towards sensitive groups.
Extending the experiments in the paper by adding another sensitive feature into considcration to Lest whether the three methods proposed by the author still putperform ERM in achieving El Fairness the case when multiple sensitive features have to be accounted for, we have decided to run the experiments the error rate/E) Disparity tradeoff with an additional sensitive feature for German Statlog Credit, ACSIncome-CA and Default of Credit Card Clients datasets. For that, used the formula for each method of defining the loss function, and instead of having single penalty term U, we summed the penalty terms for all sensitive features and multiplied them by the weighting factor. Hence the loss becomes:
N Σ
min SEF
(1 (1~1)
El f(x.))
+ Σ Use
2=11
For the German Statlog Dataset in addition to the "age" feature, the "sex" feature was used for the multiple sensitive feature experiment. Similarly, we extend the sensitive feature set the ACSIncome-Ci CA Dataset to contain not only "sex" but also 'age". We assume all sensitive features to be binary, hence we split the "age' feature using a cutoff of 30 and encoding all values less than 30 as O, and the other ones as 1. Since both features were already labeled as sensitive in the other dataset, we concluded that they were appropriate

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choices for the multiple sensitive feature experiment. presented in Appendix D. for each dataset separately.
The data biases for both Ser and Age groups are
Results of the tradeoff experiments for the German Statlog Dataset represented in Table 9 suggest that the Covariance-basee method achieves a similar accuracy as ERM with a slightly lower EI disparity, whereas KDE based method has a drop of accuracy by 0.059 and the decrease in El Disparity by 0.057. The Loss-based method for the setting with multiple sensitive features resulted in increased disparity, which contradicts the claim. We suspected that it was due the group imbalance after splitting the dataset, and we tried to mitigate that by shuffling the dataset before splitting it. This did not yield any substantial improvement, hence, we concluded that this is the result of a conflict between the sensitive features. The algorithrms may discontinue converging at point where the disparity for one sensitive feature cannot be further improved without increasing the disparity between groups of another sensitive feature. From the results of the experiment, we infer that claim only holds for KDE and Dovariance-based methods.
Testing the performance of EI in promoting long-term fairness for the case of multiple sensitive features to validate claim 2 for the case of multiple sensitive features, we ran the experiment when there were two sensitive features (each having two groups). To account for the disparity values of multiple sensitive features, instead of looking for thresholds that minimize the disparity concerning one sensitive feature, we add the disparities between groups for all sensitive features and seek thresholds that minimize this sum. By minimizing the sum we account for the overall disparity across sensitive features. Even though doing so might increase the disparity for one of the sensitive features, it would mean that the disparity for the other sensitive feature (that has been much larger), was decreased, effectively decreasing the sum of disparities and resulting in lower overall disparity. We considered four scenarios of group distributions given sensitive features, where each sensitive feature had 2 groups: (i) 21: (0, 1), (1, 0.5), x2: (0, 0.5), (1, 1); (ii) 21: (0, 0.5), (1, 1), 22: (0, .5,, (1, 5); (iii) z1:(0, .5). (1, .5,, 22: (0, 2). (0, 1); (iv) 21: (0. 2), (0, 1), 22: (0, 1), (1, 0.5). Figure 7 illustrates the evolution of the two groups concerning a sensitive feature, when different algorithms are applicd. Though the results for individual distributions are slightly inferior to those in the original paper, considering that there are multiple sensitive features concerning which the group distance has to be minimized, it was expected. Moreover, El still results in the lowest distance for almost all distributions in t=3 iterations. Figure shows the change of distance between groups over several iterations. For both sensitive features, El results in the lowest disparity for all distributions, supporting claim 2.
Eebl
Locg Aegi: Undiagoocs
0.2
[12
2.5
7.5
2.5
2.
5.0
0.0
Round
ERM
DP
BC
BR
ILFOR
(Oms)
(a) Long-term unfairness with respect to 21 multiple iterations.
(x)
Long Jermn Uniaiggon
0.6
2.5
7.5
2.5
5.0
0.3
0.0
Round
LRM
ILECR
(Ouns)
(b) Long-term unfairness with respect to 22 over multiple iterations.
Figure 2: Long-term unfairness multiple iterations for different sensitive features. The figures illustrate
that the disparity between the sensitive group probability distributions reduces faster for the EI classifier than for the other metrics, indicating that it is more favourable for achieving long-term fairness.

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Overfitting robustness check with more complex model: in order to verify claim 3, we trained complex Multi-Tayer Perceptron (MLPP) subset of the ASC-Income dataset, specifically comprising the first 1000 samples. Given the limited size of the subset, we opted for an MLP with 10 hidden layers, each consisting of 800 hidden neurons, expecting it to exhibit overfitting. Analyzing the results, depicted in Table indicated that both the KDE-Based and Loss-Basee method exhibit clear overfitting, with training accuracies and ET disparities being significantly lower than the test ones. Therefore, the proposed methods cannot be substantiated as resistant to overfitting on an overparametrized model.
Metrie
ERM
KDE-Basee
Loss-Based
Train Error
.079 + .012
.094
+ .014
.126 + .015
Test Error
.221 + .008
.238
+ 0023
.223 + .015
Train EI Disp.
.010 .005
.016
.014
.008 + .006
Test ET Disp.
.054
.009
.050
+ .044
.024 + .014
Table 5: Error rate and EI disparitics of ERM and the proposed El-regularized methods an overparame- terized Multilayer perceptron (MLP) using subset of ACS-Income dataset. The error rale and EI disparily values for all methods are indicative of overfitting, with considerably lower error rate and El disparity for the train set compared to the test set.
5 Discussion
In this study, we conducted multiple experiments to replicate and validate the key findings from the original roscarch on Equal Improvability (EI). Our efforts generally confirm the original claims, successfully repro- ducing most of the initial findings across various datasets and models. We examined three primary claims made by the authors, finding them largely substantiated, with some exceptions in specific scenarios.
During our investigation, we encountered minor discropancics, especially when introducing additional sen- sitive features and using different datasets. To address these inconsistencies and gain further insights, we reached out to the original authors for clarification and additional resources. They promptly provided sup- port, which helped us understand the nuances better, though some results remained challenging to replicate precisely.
Additionally, we explored the question of the EI approach's 's robustness against overfitting. The results were not consistent; while El performed well under certain conditions, it did not consistently demonstrate immunity to overfitting, particularly with complex models. This highlighted the need for cautious application and potential euhancement the El method in environments susceptible to overfitting.
To further test the robustness and applicability of the El approach, we expanded our experiments with the Default of Credit Card Clients Dataset and found that the El approach still held up well, reinforcing the validity of the original conclusions. Moreover, we conducted experiments with the integration of multiple sensitive features, providing a more comprehensive assossment of the EI method's efficacy in diverse scenarios.
To conclude, our study not only confirms the effectiveness of the El approach but also sheds light on its limitations. While El holds promise for enhancing Al fairness, our findings highlight the need for further rescarch into more adaptable approaches that can effectively address the diverse challenges of AI applications.
5.1 What was easy
Communication with authors:
The communication with the authors of the study was notably easy and efficient, which greatly facilitated the reproduction process. The authors consistently provided prompt responses, ensuring that any queries or clarifications needed were addressed quickly. Refer to section 5.3
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Reproducing results for synthetic dataset:
The reproduction of results for the synthetic dataset was remarkably smooth and efficient, largely due to the immediate availability of all necessary files. This complete access enabled straightforward replication process. Additionally, the results we achieved closely aligned with those published in the paper, reflecting the study's reproducibility and reliability for this dataset. Furthermore, the experiments benefited from a notably short runtime, which significantly streamlined our overall effort in this part of the study.
Clarity and Quality of the Paper:
The paper was well-written, conciso, and successful in clearly conveying its general message. This case of understanding greatly assisted in grasping the study's concepts and objectives, contributing to a smoother reproduction process.
5.2 What was difficult
Environment Setup and Dependency Issues: The initial challenge was in setting the required environment. The provided requirements file had incorrect dependoncies, which hindcred the creation of a compatible environment. We had to revise and adjust the dependency versions to ensure their mutual compatibility, which was more time- consuming than anticipated.
Datasct-Specifie Hyperperameters: A significant issue the lack of clarity regarding hyperparameters used for different datasets, namely the Income and German Datasets. The original paper and the accompanying GitHub repository did not specify distinct hyperparameters for each dataset, leading to initial difficulties in reproducing the results. Through direct communication with the authors, we obtained the missing code and hyperparameters. Although we were unable to replicate the exact mumerical results, the overall conclusions of the study were consistent with the authors' claims.
Adaptation for Table Reproduction: In our attempts reproduce Table 4, we faced hurdle when adjusting the number of weights for a layer. The method for calculating optimal effort became inapplicable, and guidance for adjustments was absent in both the code and the paper. This omission required us to develop an undorstanding and approach independently, illustrating the need for more detailed documentation in research.
Code Limitation for Specific Scenarios: Another challenge was the limitation of the provided code, which was tailored for specific scenario involving data with single binary sensitive feature. To address this, we expanded the scope by adapting the code for multivariate cases, allowing it to handle multiple binary sensitive features. This extension enabled us to conduct further experiments to assess whether EI (Equality of Impact) outperforms other fairness methods in more complex settings. This adaptation highlighted the necessity for more versatile and inclusive code in research studies.
5.3 Communication with original authors
Initially, we contacted the authors to inquire about the hyperparameters used. They prompily responded within day, guiding us on the appropriate parameters and providing the Logistic Regression (LR) notebooks for the Income and German datasets. However, notebooks detailing the tradeoff analysis were absent. Following their instructions, we created these notebooks ourselves, but our results diverged from those reported in the original study. This led to second communication with the authors, who quickly supplied the missing tradcoff notcbooks for both datascts. Using these notcbooks, our subsoquent experiments produced patterns similar to those in the original paper, yet with some variances. The authors have acknowledged these discrepancies and expressed their intention to update the repository, which we believe may resolve these issues. As of this report, we are awaiting this update. Furthermore, we noted the absence of Multi- Layer Perceptron (MLP) files needed to reproduce two tables from the article. Upon requesting these from the authors, they promptly sent us the required files. In total, we contacted the authors thrice, each time marked by their swift and helpful responses. Despite their assistance, replicating the exact results for the Income and German datascts remains a challenge. We are optimistic that the upcoming repository update will aid in resolving these discrepancies.
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6 Aknowledgements
We would like to thank Alexandre da Silva Pires for his supervision and valuable support during the devel- opment of this work.
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Published in Transactions on Machine Learning Research (07 (07/2024)
References
U.S. Energy Information Administration. U.s. electricity net generation and resulting co2 emissions by fuel in 2022. httpa///00//////////////////////
eiw. gww.g/oooll///g//aaagaaggoo 1ttps//ffaaapppp1a777t1111111
2023.
Larson Mattu S. Kirchner L. Angwin, Machine bias there's software used across the country to predict future criminals. and it's biased against blacks. ProPublica, 2016.
J. Casillas. Bias and Discrimination in Machine Decision-Making Systems., volume 41. Springer Nature Switzerland, 2023.
Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A fair classifier using kernel density estimation. In Advances in Newral Information Processing Systems (NeurIPS), volume 33, pp.
2020.
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. Retiring adult: New datasets for fair machine learning. In Advances in Neural Information Processing Systems, volume 34, 2021.
Dheeru Dua and Casey Graff. UCT machine learning repository. htttp://archive.. ics. ics.uci.edu/ml 2017.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. Fairness through awareness.
2011.
Google. Google data center pue performance. efficiency/ 2023.
httpss//wrrr.gogoggaaa/////bbbbt
Ozgur Guldogan, Yuchen Zeng, Jy yong Sohn, Ramtin Pedarsani, and Kangwook Lee. Equal improvability; A new fairness notion considering the long-term impact. 2023.
Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasuuramaniaa. Equalizing recourse across groups. 2019.
Moritz Hardt, Eric Price, and Nathan Srebro. Equality of opportunity in supervised learning. 2016.
Hoda Heidari, Vedant Nanda, and Krishna P. Gummadi. On the long-term impact algorithmic decision policies: Effort unfairness and feature segregation through social learning. 2019.
I-Cheng Ych. Default of credit card clients. http://bbb.or/11..2433//////
UCI Machine Learning Repository, 2016.
DOI:
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi. Fairness Constraints: Mechonisms for Fair Classification, volume 54. 2017. c.
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Published in Transactions on Machine Learning Research (07 (074)
A Appendix
A Equal Improvability equation
In the work of Guldogan et al. 2023). the principle of Equal Improvability is formulated through the following
equation:
P(max f(x + Ax) 0.5 f(z) < 0.5, = 2) = (maxp(Ax)s) ( f( + Ax) 2 0.5 f(x) < 0.5)
(x) 0.5 indicates samples labeled by the classifier, and [(x) 0.5 indicates that the sample labeled as 1. The formula for El ensures that samples belonging different groups that classified 0 are equally likely to be classified 1 after applying effort. The formula for can vary for different cases.
B Reproducibility results
B.1 Error rate and El disparities of ERM and the three proposed El-regularized methods MLP
Dataset
Metric
ERM
Covariance Based
KDE-Based
Loss-Based
Synthetic
Error Rate
.205 + .003
.244 + .007
.227 = .009
.226 + .009
EI Disp.
.140 = .036
.004 + .002
.010 = .005
.012 - .009
German Stat.
Error Rate
.221
.010
.999 = .012
.241 .022
.238 .035
El Disp.
.059 .046
.013 + .025
.045
.058
.013 .019
ACSIncome-CA
Error Rate
.181 + .002
.202 + .002
.184 + .002
.185 + .002
El Disp.
.034 = .001
.012 = .010
.008 .004
.003 = .002
Table 6: Error rate and disparities of ERM and the three proposed El-regularized methods on Multilayer perceptron (MLP). The results indicate that El-based classifiers have a lower El disparity without causing significant increase in the error rate, illustrating their reduced bias towards sensitive groups.
B.2
Long-term (un)fairness check:
(iv)
Long torm Unfairess
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
2.5
5.1
7.5
1.0
2.5
5.0
7.5
0.3
2.5
5.0
7.5
1.0
2.5
5.0
7.5
Round
ERM
DP
BE
ILFCR
(Ours)
Figure 3: Long-term unfairness at each round t for various algorithms.
Figure 4: Long-tern term unfairness over multiple iterations. The figure illustrates that the disparity between the sensitive group probability distributions reduces faster for the El classifier than for the other metrics, indicating that it is more favourable for achieving long-ter term fairness.
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Published in Transactions on Machine Learning Research (07 (07/2024)
ristriantion
FL
Groep Choup
0.5
div 0.51
drv 0.30
dr 0.20
dry 0.22
Mry 0.20
Fiy 320
Trv 0,50
0.0
0.5
fry UM
dry
drv
dry
Arv
[1.1
Deasly
0.0
183 0.5
dyy
Arv 0.21
Ary - 1.28
dye all
Arv 0.30
Kyy = 2.11
Myv 0.08
0.1
0.0
0.0
0..25
Myp 0.82
div
0.0
Hov 0.06
div 0,6
div
ALv
Miv 301
[1.1-
0.00
0.0
20
20
20
0
20
Figure Probability distribution for each sensitive group after updating improvable features using each algorithm for 3 iterations. In comparison current fairness notions, such as Demographic Parity (DP), Bounded Effort (BE), Equal Recourse (ER), and Individual-Level Fair Causal Recourse (ILFCR), El results in reduced gap in feature distribution across different groups.
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Published in Transactions on Machine Learning Research (07 2024)
B.3 Numerical discrepancies in Error rate and El disparities
In the tables below we depict the numerical discrepancies in Error rate and El disparities of ERM and the three proposed El-regularized methods for a Logistic Regression and Multilayer Percept We present it as difference between the values obtained by the team and the values reported in the original paper.
We suspect that the numerical variations could stem from the authors using varying seeds hyperparameters in their experimental framework, detail not mentioned in either the formal report or the official code repository.
Dataset
Metric
ERM
Covariance-Based
KDE-Based
Loss- Based
Syuthetic
Error Rate
.001 + .001
.000 + .000
.003 + .006
.000 + .001
El Disp.
.001 + .000
.001 = .000
-000 = .000
.000 = .000
German Stat.
Error Rate
.000 + .000
.000
.000
.006
.007
.000
.000
EI Disp.
.000 = .000
.001 = .004
.19 = .362
.001 = .004
ACSIncome-Ca
Error Rate
.001 = .000
.000
.000
.000
.001
.002 .000
El Disp
.000 + .000
.000 .000
.000
.000
.000 .000
Table Logistic Regression (LogReg)
Datasel
Metrie
ERM
Covariance- Baaeraanne-Basedd
KDE- Based
Loss- Bass-Based
Synthetic
Error Rate
.000 + .000
.002 + .001
.000 + .001
-003 + -.003
El Disp.
-.001 + .000
.000 + .000
-.001 + -0001
-000 + .000
German Stat.
Error Rate
.000 + .000
.000 + .000
.009
.004
.000 + .000
El Disp.
.000 = .001
.000 = .000
.004 .033
.000 = .000
ACSIncome- CA
Error Rate
.000 + .000
.000 + .000
.002
.000
.000 + .001
El Disp.
-000 -001
.002 + .004
-002 + .002
-003 -001
Table 8: Multilayer perceptron (MLP)
C
Extended analysis
Datasct
Metric
ERM
Covarianco-Basse
KDE-Based
Loss-Basee Based
German Statlog
Error Rate
0.214 0.015
0.214 0.016
0.273 0.034
0.295 0.018
El Disparity
0.121
0.037
0.120 0.040
0.064 0.087
0.400 0.490
ACSIncome
Error Rate
0.184 + 0.000
0.200 + 0.000
0.196 + 0.000
0.194 + 0.000
El Disparity
0.031 0.001
0.008 0.001
0.005 0.001
0.006 = 0.002
Default of Credit Card Clients
Error Rate
0.174 + 0.001
0.177 + 0.001
0.175 + 0.001
0.191 + 0.003
El Disparity
0.013 + 0.002
0.003 + 0.001
0.003 + 0.000
0.003 + 0.002
Table 9: Error rate and EI disparitics of ERM and the three proposed EI-rogularized methods on Logistic Regression (LogReg) trained using two sensitive features. results indicate that El-based classifiers have lower EI disparity without causing a significant. increase in the error rate, illustrating their reduced bias towards sensitive groups. However, the Loss-based classifier failed to achieve lower El disparity value than ERM classifier.
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Published in Transactions on Machine Learning Research (07/2024)
C.1 Results of evaluating El on the Default of Credit Card Clients Dataset
Default of Credit Card
Comstinos--aaed
Firor Rate
KTDFaaaeed
0.176
0.174
0.000
0.002 El Disparity
0.004
Figure 6: Tradeoff between EI disparity and error rate for the Default of Credit Card Dataset using Logistic Regression (LogReg). All three int roduced methods, i.e. Covariance-basedd KDE-based, and Loss-based EI, successfully find a tradeoff between the error rate and El disparity, being the bottom left corner of the figure.
C.2 Long-term faimneir evolution with multiple sensitive features
Indirl ditribatiion
E3M
DP
BE
ER
FCR
0.3
My
1.0
2 0.3
Kry
dry C.
Ary - 011
dry
dee ALL
3.1
Dawso
3.0
0.3
Ary
Ap 888
0.0
3.0
3 0.22
Avp
Kry 160
dry 028
dry cos
day Mi-iiia (33
Mry CM
dex- 200.
3.1
20
20
20
3
20
11
20
201
20
(a) Sensitive feature 21.
Trrtal demivrzionn
BPM
DP
7B
T.FOR
.1.5
try
Apy - 3.3
tty
dpy
Apy
fyy -
13.00
0.5
Hry-os tt
Apy - 3,80
Mry 30
dpy 3.11
Myy U.
fy will
Ny 100
0.1
0.0
Ra 1952
Sro
Apy -
dpy
Spy
fly
0.00
2 0.5
fry 033
dry 1134
fry 021
4rv 131
Fyr - 0,30
fry w12
0..
20 0.0
0
20
20
(b) Sensitive feature z2.
Figure 7: Probability distribution for each sensitive group after updating improvable features using each algorithm for 3 iterations. In comparison to current fairness notions, such as Demographic Parity (DP), Bounded Effort (BE), Equal Recourse (ER), and Individual-Level Fair Causal Recourse (ILFCR), El results in a reduced gap in feature distribution across different groups.
15

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Published in Transactions on Machine Learning Research (07 (07/2024)
D
Bias in the utilized datasets
D.1 Gender bias
Credit risk [Females]
Income
$50k (Females]
Default payment next month [Females]
No
No
Default
No
Yes
No
Yes
Default
No
payment
(a) Credit dataset females
(b) German dataset females
(c) Income dataset females
Default payment next month [Males]
Credit risk [Males]
Income $50k [Males]
No Default
Default
Yes
Yes
payment next month
No
Yss
(d) Credit dataset males
(e) German dataset males
Income dataset males
Figure 8: Gender bias in various datasets
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Published in Transactions on Machine Learning Research (07 (07/2024)
D.2 Age bias
Credit risk [Age 30]
Income > $50k (Age 30]
No
No
Yes
Yes
No
Credit dataset age 30
Income dalaset age 30
Credit risk (Age 30)
Income S50k (Age 30]
No
Yes
Yes
Yes
tes
(c) German dataset age below 30 (d) Income dataset age below 30
Figure 9: Age bias in various datasets
17

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Published in Transactions on Machine Learning Research (07/2024)
E
Training runtimes and energy consumption for the conducted experiments
We strive to calculate the impact of our experiments on the environment. To this end, calculate the carbon footprint using the following formula.
CO2e = CI PUE Power * time
For running the experiments we employed NVIDIA T4 GPU, being available Google's data center facilities. As those include servers in various locations in the United States, e.g. Oregon and Iowa in case, to calculate the total carbon emission we take advantage of the average PUE (Power Usage Effectiveness) reported by Google being 1.10 and the approximated carbon intensity (CI) of 0.86 pounds of CO2 emissions per kWh Google 2023 Administration 2023 Based on these figures, the total carbon emission was
calculated to be approximately 30.708 lb. CO2e ie. 13.929 kg COze. See Table 10 for all runtime details.
Dataset
Experiment
Model
Sensitive attrs.
Device
Runtime
Est. EC
Synthotic Synthetic
Training
LogReg
1
CPU
min
Tradeoff
LogReg
1
CPU
36 min
German Stat.
Training
LogReg
1
CPU min
German Stat.
Tradeoff
LogReg
1
CPU min
German Stat.
Training
LogReg
2
CPU min
German Stat.
Tradeoff
LogReg
2
CPU 7 min
German Stat.
Training
MLP
1
CPU 2 min
German Stat.
Training
DNN
1
CPU 5 min
ACSIncome-CA
Training
LogReg
1
GPU
3.5 h
0.116 kWh
ACSIncome- CA
Tradeoff
LogReg
1
GPU
6
0.198 kWh
ACSIncome-Cr
Training
LogReg
2
GPU
5
0.165 kWh
ACSIncome CA
Tradeoff
LogReg
2
GPU
6.3 h
0.281 kWh
ACSTncome-CH
Training
MT.P
GPU
6.5 h
0.215 kWh
Default of Credit Card Clients
Training
LogReg
1
GPU
0.22 h
0.007 kWh
Default of Credit Tradeoff
LogReg
1
GPU
2.3 h
0.076 kWh
Card Clients
Default of Credit Training
LogReg
2
GPU
0.34 h
0.0112 kWh
Card Clients
Table 10: Training runtimes on various datasets accompanied by GPU energy consumption estimates.
18


Reviews:
[{"review_id": "5s8SyubR5H", "reviewer": "Reviewer_nWRX", "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\nThe paper is well written. The claims from the original paper being assessed are well described and the contributions are well stated. As a reproducibility study, the authors made an effort go beyond the experiments in the original paper and explore the performance of the model on a new dataset as well as in a different setting (from a single sensitive attribute to multiple attributes). The results are well explained in cases where their is a discrepancy with the original paper. \n\nWeaknesses:\n\nA limitation in the experiment resides in mainly comparing the 3 training strategies for enforcing EI with ERM. While this is a setting in the paper, interesting results in the original paper are in the comparison with similar dynamic fairness metric (BE and ER). Indeed, the Figures 4 and 5 in the original paper which compare to BE and ER show minimal improvement of EI compared to those methods. In the reproducibility study, I would have appreciated more comparison with those methods and an assessment of when EI provides substantial improvement. This can also be seen in the experiments with multiple attributes.", "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": "The paper performs a reproducibility study of the paper \"Equal Improvability: A New Fairness Notion Considering the Long-term Impact\" by Guldogan et al. (2023). The original paper proposes a new group fairness metric called \"Equal Improvability\" (EI) which aims to equalize the probability of receiving a positive outcome for a sample after improving its features over time. This EI fairness notion aims to take into account the dynamic nature of the fairness in decision making systems compared to static group fairness metrics. The original paper shows improvement of models trained to enforces EI vs ERM with minimal loss of accuracy. The paper also shows slight improvement over existing similar dynamic fairness metrics, namely \"Equal Recourse\" (ER, Gupta et al. 2019) and Bounded Effort (BE, Heidari et al. 2019).\n\nIn this reproducibility study, the authors assess the claims of the original paper pertaining to improvement of ERM in achieving EI as well as the long term properties of an EI-trained model. They also assess the overfititng properties of these EI-based training strategies. The main contributions are in (1) reproducing the results from Guldogan et al. (2023) on the same datasets (1 synthetic, 2 real) as in the original paper and on one additional real dataset, (2) extending the method in Guldogan et al. (2023) from 1 sensitive attribute to 2 sensitive attributes, and (3) assessing the robustness to overfitting using a more complex model than in the original paper (MLP vs logistic regression).", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "As specified above, I would like to see additional experiments comparing EI and BE and ER and a discussion of settings where EI provides an improvement over these comparative methods.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "sGzzassq0w", "reviewer": "Reviewer_QzBZ", "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": "Strength:\n\n- [Major] Authors understand well about the original paper, and made effort to communicate with the authors of original paper. Overall, this paper is a relatively complete reproducible study.\n- [Major] In general, this paper is well-written. But as I mentioned below, there are still many places can be improved.\n\nWeakness:\n\n- [Major] Many details of the experiment setting are missing, thus hard to justify whether the settings are reasonable or not and whether the conclusions are correct or not. For instance, How did you extend the experiments into multiple sensitive feature case? Is there any code? The equation 2 is not very informative, and seem not the correct way of extending their experiments into multiple sensitive feature case (we can discuss more if the authors can provide more detailed explanation). Moreover, why the results of the existing experiments are different from the original paper. Do you use different seed?\n- [Medium] Many decisions made by this paper is not well justified. For instance, the third claim listed in the paper does not seem to be a major claim of the EI paper. Why do authors choose this claim? In contrast, there are other interesting claims which plays a more important role in their paper. For instance, to motivate their proposed fairness notion, they pointed out the problems of similar fairness notions such as ER and BE. Replicating their experiments or trying some other possible reasonable data distributions and see whether the conclusions they made still hold will be more interesting.\n- [Minor] There are some typos in the manuscript. Many links are broken, too. Here are some issues that I found, and there may exist more.\n    - Five lines above the Sec. 3, the link of “section 4” is broken.\n    - The citation style sometimes are incorrect. For instance, in the fourth line under Sec 3.1, it should be “chance of acceptance (Guldogan et al., 2023).” instead of “chance of acceptance.  Guldogan et al. (2023).” There are many such issues, which could be fixed by using commands like `\\citep` instead of `\\cite` or `\\citet`.\n    - What is the meaning of“[NEW]” for the paragraph “Robustness Check Against Overfitting” in page 5?\n    - The bottom line of Table 4 is missing.\n    - In Table 4, the “DP,” “EO,” “EODD” should be “DP Disp.,” “EO Disp.,” and “EODD Disp..”\n    - Link of “section 6.4” in the last second line of page 8 is broken.\n- [Minor] The clarify of this paper can be improved. For instance, in the table 1, there is a column \"sensitive attrs.\", but it is not very clear what does \"1\", \"2\", and \"1&2\" mean. Meanwhile, many notations in the paper are not explained at all (but it is not a huge problem since authors are using exactly the same notations as the original paper, which has been explained well).", "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": "In general, this paper conducts reproducibility study of the paper \"Equal Improvability: A New Fairness Notion Considering the Long-Term Impact\", especially three claims: (i) the three proposed EI learning methods can reduce EI disparity (especially when comparing to ERM), (ii)  EI accelerates the process of mitigating long-term fairness under the dynamic scenario, and (iii) their proposed approaches do not cause overfitting when the model is over-parameterized.\n\nTo verify the three claims, the authors communicated with the authors of that paper, and run the corresponding experiments to verify whether the results provided by the paper is reproducible or not. In addition to that, the author also tried experiments beyond the original paper, including evaluating the proposed methods on a different dataset, and extending the experiments in the paper by considering the scenario of multiple sensitive features.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- [Major] Please provide more details of the experiment setting.\n    - For the results that lead to a strong conclusion (especially when fail to reproduce the consistent results as the original paper), it is important to report the detailed experiment setting to help reviewer understand what is going on.\n    - Does the equation 2 mean that you simply adding the penalty obtained from each sensitive attribute? I want to suggest to combine multiple sensitive attributes into one, and this is what most of the existing fair learning papers do. To be more specific, for instance, assume two sensitive attribute: gender (female & male), and race (white & black), we can combine them into one attribute named sensitive (white female & black female & white male & black male), and then we should still only have one penalty term.\n- [Medium] The choice of claim 3 is a bit random and lack of justification. While the results seem to be helpful, more details about the experiment setting is needed to verify the reliability of the conclusion. Moreover, it would be great to add experiments on EI v.s. BE and EI v.s. ER.\n- [Minor] Fixing the typos and cleaning up the draft is needed.", "broader_impact_concerns": "No", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "qscmxmDhAE", "reviewer": "Reviewer_Bho8", "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 authors comprehensively evaluated the methods/results of the paper of Guldogan et al. 2023. \n- The authors were able to validate many of the claims in the original paper and if they could not they highlighted what the discrepancies were exactly. \n- The authors did additional experiments to evaluate the pros/cons of the EI.\n\nWeaknesses:\n- I do not see any major weaknesses in this paper.", "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 work evaluates the robustness of Equal Improvability (EI), which is an effort-based framework for ensuring long-term fairness. To do so, the authors analyze three EI-regularization techniques, covariance-based, KDE-based, and loss-based. The findings largely agree with previous results that these techniques provide enhanced performance over normal empirical risk minimization (ERM). Furthermore, the authors uncover that EI ensuring methods are not always robust to overfitting. Lastly, the authors evaluate EI on a new dataset and extend previous experiments by adding other sensitive attributes. In conclusion, the study confirms that EI is effective find that the primary claims made by the authors of the original work are substantiated by the results produced in this work.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- In the 2nd paragraph on page 7, z1 and z2 should be written in math mode.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Overall, the paper performs a thorough reproducibility study of the original Equal Improvability paper, confirming many of the original claims and extending the analysis to new datasets and settings. Reviewers appreciated the effort to communicate with the original authors, the well-written nature of the paper, and the (added) details needed for reproducing the reproducibility study. The paper meets expectations of the TMLR reproducibility paper.
Decision: Accept (poster)