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REVIEW 3 major objections 5 minor 31 references

Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper presents FairPrep, a modular benchmarking framework it positions as the first focused on group fairness through pre-processing, and reports that Reweighing and Optimised Pre-processing give the most balanced fairness-utility trade

desk verdict FairPrep is a useful software contribution, but the empirical comparison that anchors the paper is not supported by the reported experiments—the LFR prose contradicts Table 1. read the letter →

arxiv 2508.15193 v1 pith:6WIOBM4V submitted 2025-08-21 cs.LG

Brodie Oldfield , Ziqi Xu , Sevvandi Kandanaarachchi This is my paper
classification cs.LG
keywords fairness-awarepre-processinggroupfairnessbenchmarkingframeworktabulardatareproducibilityfairness-utilitytrade-offreweighingoptimised
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces FairPrep, a modular framework for benchmarking fairness-aware pre-processing methods on tabular data. Its central claim is that this is the first benchmarking study aimed specifically at group fairness achieved through pre-processing, where data is transformed before any model is trained. The authors argue this matters because pre-processing methods are model-agnostic and privacy-friendly but have lacked standardised evaluation tools. On five common datasets and four pre-processing methods, they find that Reweighing and Optimised Pre-processing offer the most balanced fairness-utility trade-offs, while Learned Fair Representations can severely distort the data and Disparate Impact Remover changes little on its own. If the claim stands, FairPrep gives the field a common baseline for comparing data-level fairness methods.

What carries the argument

The central object is FairPrep's two-stage pipeline: a pre-processing stage that applies a chosen fairness intervention, caches both original and processed data, and computes data-level metrics; and a benchmarking stage that trains models on both versions, then sweeps thresholds from 0.01 to 0.99 to trace fairness and utility curves. Modular interfaces for datasets, interventions, and models carry the argument that any pre-processing method can be plugged in and compared on equal footing.

What would settle it

Run the public repository end to end and check that Table 1 and the threshold-sweep figures reproduce exactly; separately, a literature search for any earlier evaluation study scoped specifically to group fairness via pre-processing would test the 'first benchmark' claim.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that a standardised, modular benchmark for data-level fairness is both needed and feasible, and FairPrep is that benchmark. The framework separates a pre-processing stage, which transforms and caches data and reports data-level fairness metrics, from a benchmarking stage, which trains models on original and transformed data under a holdout split and sweeps classification thresholds from 0.01 to 0.99 to expose fairness-utility trade-offs. Applied to four pre-processing methods, Reweighing, Learned Fair Representations, Disparate Impact Remover, and Optimised Pre-processing, on five tabular datasets, the experiments show Reweighing achieves perfect gr

Load-bearing premise

The claim that FairPrep provides a reproducible standard depends on the public code repository actually implementing the described two-stage pipeline and producing the same numbers, but the paper gives no commit hash or versioned configuration to pin that down.

Editorial extensions

If this is right

  • FairPrep provides a common pipeline so future pre-processing methods can be compared against Reweighing, LFR, DIR, and OPP without re-implementing data preparation.
  • The threshold sweep turns fairness from a single number into a curve, revealing that fairness on unprocessed data can be unstable across decision thresholds.
  • Given the reported results, practitioners seeking controlled fairness gains on these datasets should consider Reweighing or Optimised Pre-processing before LFR or DIR.
  • The YAML batch interface allows systematic variation of datasets, methods, and models, which should reduce inconsistencies in fairness evaluations.
  • The reported preference among methods is contingent on the binary-classification, tabular-data setting the framework currently covers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural test of the modularity claim is whether a new classifier or a multi-class fairness method can be added without changing the core pipeline; if it can, the framework could become the default harness for data-level fairness work.
  • The full threshold-sweep view suggests that fairness-as-a-curve may be a more deployment-relevant standard than any single metric, an extension the authors only gesture at.
  • The reproducibility guarantee is only as strong as the public repository at the moment it is run; pinning a commit hash and versioned configurations would harden the claim.
  • Because only one predictive model is demonstrated downstream, the ranking of the four methods could shift with other model classes; repeating the pipeline with tree-based and deep models is a straightforward extension.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces FairPrep, a modular two-stage benchmarking framework for evaluating pre-processing group-fairness methods on tabular data. Built on AIF360, it provides a pre-processing stage that computes distributional/group metrics before model training and a benchmarking stage that trains predictive models on original and transformed data, sweeping decision thresholds and reporting fairness and utility metrics. The framework is demonstrated on three datasets in the pre-processing stage (Adult, COMPAS, German Credit) and on Adult with Logistic Regression in the benchmarking stage, using four pre-processing methods (Reweighing, LFR, DIR, OPP). The paper claims to be the first benchmarking study focused specifically on pre-processing group fairness, and its empirical discussion suggests that RW and OPP offer more balanced fairness-utility trade-offs than LFR or DIR.

Significance. If the framework and reported numbers are reliable, FairPrep addresses a real gap: pre-processing fairness methods lack standardized, reproducible evaluation pipelines. The modular design, YAML batch interface, and use of established datasets/metrics are sensible and potentially useful to the community. The strongest concrete contribution is the framework itself plus the claim of a reproducible benchmark. However, the empirical support for the headline comparative conclusions is currently thin and internally inconsistent in places. No mathematical circularity is present; the main risks are empirical and reproducibility-related.

major comments (3)
  1. [§3.2 / Table 1] The text states that LFR 'often remov[es] all positive labels (Adult, COMPAS)' (Section 3.2, item 2), yet Table 1 lists 4,664 Adult positives and 161 COMPAS positives under LFR. Moreover, for German Credit, LFR shows base rate 1.000, 1,000 positives, and 0 negatives, yet the reported empirical difference is 0.798. Under the §2.2.1 definition of empirical difference as a disparity of a selected metric between privileged and unprivileged groups, an all-positive label vector should yield equal group metrics and hence a disparity of 0. Either the metric definition, the implementation, or the reported numbers are mis-specified. Because the benchmark's credibility rests on these reference numbers, this inconsistency must be resolved.
  2. [§3.3 / Figure 1] The benchmarking stage is reported only for the Adult dataset with Logistic Regression and only for original data versus Reweighing. The paper's central empirical claim—that RW and OPP offer more balanced fairness-utility trade-offs than LFR or DIR—is not demonstrated by downstream model results. Section 3.2 draws conclusions about LFR and OPP from pre-processing metrics (Table 1), which measure label distributions and not model utility. To support the headline comparison, the benchmarking stage needs to include at least OPP and preferably LFR/DIR, on more than one dataset/model, with fairness and utility metrics reported.
  3. [§3.1 / Abstract] The reproducibility claim is not independently verifiable from the manuscript. The text says 'The full experimental results can be reproduced via the link provided in the abstract' (Section 3.1), but the paper provides no commit hash, no artifact appendix, no versioned configuration files, and no explicit seeds or error bars. Additionally, LFR and OPP hyperparameters are unreported; these methods are known to be sensitive to settings such as number of prototypes, optimization epochs, and distortion constraints. For a benchmarking standard, this is a load-bearing omission: a benchmark whose configuration cannot be reproduced does not yet serve as a standard.
minor comments (5)
  1. [§2.2.1] The definition of 'empirical difference' is vague: it says 'disparity of a selected metric between privileged and unprivileged groups' but does not state which selected metric is used in Table 1 or how it is computed. Please clarify.
  2. [§2.4] Typo: 'The proposed FairPrep involve' should be 'involves'. Also consider naming the related-work tool 'Fair-IRT' consistently with the reference [23].
  3. [Figure 1] The five subplots are not individually labeled in the caption; the reader cannot tell which fairness metric each subplot corresponds to. Please add subplot titles or a legend.
  4. [§1] The novelty claim 'first benchmarking study focused specifically on group fairness through pre-processing techniques' is strong. Please soften or provide a more systematic comparison with existing benchmarks (e.g., FFB [9]) to justify uniqueness beyond the focus on the pre-processing stage.
  5. [§3.1] The holdout split is stated as 'e.g., 70% training, 15% validation, 15% testing', but the actual split used in the experiments is not specified. Please report the exact split and any cross-validation strategy.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FairPrep is a self-contained software/benchmarking contribution with no fitted-then-predicted derivation, and the self-citations are not load-bearing.

full rationale

This is a software and benchmarking paper, not a predictive or mathematical derivation, so the circularity patterns do not apply. The reported comparisons (Table 1, Figure 1) are produced by applying standard AIF360-based metrics to original and pre-processed datasets and then to model predictions; no parameter is fitted to the target comparison and later reported as a prediction. The self-citations ([18], [23], [25], [26], [28]) appear only in related-work positioning, e.g., “Tools such as AIF360 [5], UST [26], and FairLearn [22] focus on model-level adjustments,” and they do not constitute the justification for FairPrep’s design or for the empirical ranking of RW/OPP versus LFR/DIR. The “first benchmarking study” novelty claim is an unverified assertion, not a reduction to an input. Two separate correctness concerns should be weighed elsewhere: the prose in Section 3.2 about LFR “remov[ing] all positive labels” conflicts with Table 1’s positive-label counts, and the reproducibility promise in Section 3.1 is not machine-checkable from the text because no commit hash or artifact appendix is provided. These undermine trust in the reported numbers, but they are not circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 1 invented entities

The paper contributes a software framework rather than a mathematical derivation. The main assumptions are that the chosen datasets represent fairness-critical practice, that the AIF360 wrappers match the original methods, and that the selected metrics capture the fairness construct. Unreported method hyperparameters are the main unstated degrees of freedom.

free parameters (1)
  • Unreported LFR and OPP optimization hyperparameters = not reported
    FairPrep's headline comparisons in Section 3.2 depend on AIF360 default or chosen settings for latent dimensions and optimizers, but the paper states none, making the trends hard to reproduce exactly.
assumptions (3)
  • domain assumption Benchmark datasets are representative of fairness-critical applications.
    Section 2.3 selects five 'commonly used' datasets; external validity of all method rankings depends on this.
  • domain assumption AIF360 implementations faithfully represent the published algorithms.
    Section 2.4 and the experiments evaluate RW, LFR, DIR, and OPP as bundled in AIF360; discrepancies between implementation and published method could change the results.
  • domain assumption Selected fairness and utility metrics adequately capture the fairness construct.
    Section 2.2 chooses group-fairness and accuracy metrics without justification that they cover the relevant fairness space; conclusions are metric-dependent.
invented entities (1)
  • FairPrep framework independent evidence
    purpose: Two-stage benchmarking pipeline for pre-processing fairness methods, with YAML batch execution and threshold sweeps.
    The artifact is new, but the only external falsifiable handle is an unpinned GitHub repository; no commit hash or result artifacts are provided in the paper.

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Cite this review

Pith. "Pith review of Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework." pith.science (2026). https://pith.science/paper/6WIOBM4V

@misc{pith2026250815193,
  author       = {Pith},
  title        = {Pith review of: Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6WIOBM4V}},
  note         = {Machine review of arXiv:2508.15193}
}
read the original abstract

As machine learning systems become increasingly integrated into high-stakes decision-making processes, ensuring fairness in algorithmic outcomes has become a critical concern. Methods to mitigate bias typically fall into three categories: pre-processing, in-processing, and post-processing. While significant attention has been devoted to the latter two, pre-processing methods, which operate at the data level and offer advantages such as model-agnosticism and improved privacy compliance, have received comparatively less focus and lack standardised evaluation tools. In this work, we introduce FairPrep, an extensible and modular benchmarking framework designed to evaluate fairness-aware pre-processing techniques on tabular datasets. Built on the AIF360 platform, FairPrep allows seamless integration of datasets, fairness interventions, and predictive models. It features a batch-processing interface that enables efficient experimentation and automatic reporting of fairness and utility metrics. By offering standardised pipelines and supporting reproducible evaluations, FairPrep fills a critical gap in the fairness benchmarking landscape and provides a practical foundation for advancing data-level fairness research.

Figures

Figures reproduced from arXiv: 2508.15193 by the authors.

Figure 1
Figure 1. Balanced Accuracy (left axis, blue) versus fairness metrics (right axis, red) across varying classification thresholds [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Reference graph

Works this paper leans on

31 extracted references · 19 canonical work pages

  1. [1]

    Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach. 2018. A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018 , Vol. 80. 60–69. http://proceedings.mlr.press/v80/agarwal18a.html

    work page 2018

  2. [2]

    Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu. 2019. Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019 , Vol. 97. 120–129. http://proceedings.mlr.press/v97/agarwal19d.html

    work page 2019

  3. [3]

    Agency for Healthcare Research and Quality (AHRQ). 2016. Medical Expendi- ture Panel Survey (MEPS) Panel 21 Longitudinal Data File. https://meps.ahrq. gov/mepsweb/data_stats/download_data_files.jsp U.S. Department of Health & Human Services

    work page 2016

  4. [4]

    Barry Becker and Ronny Kohavi. 1996. Adult. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5XW20

    doi:10.24432/c5xw20 1996

  5. [5]

    Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varsh- ney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extens...

    arXiv 2018

  6. [6]

    Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Nate- san Ramamurthy, and Kush R

    Flávio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Nate- san Ramamurthy, and Kush R. Varshney. 2017. Optimized Pre-Processing for Discrimination Prevention. In Advances in Neural Information Process- ing Systems 30: Annual Conference on Neural Information Processing Sys- tems 2017 . 3992–4001. https://proceedings.neurips.cc/paper/2017/hash/ 9...

    work page 2017

  7. [7]

    Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian

    Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015 . 259–268. doi:10.1145/2783258.2783311

    arXiv 2015

  8. [8]

    Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019. Fairness- Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019 . ACM, 2221–2231. doi:10.1145/3292500.3330691

    work page arXiv 2019

Show all 31 references
  1. [9]

    Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, and Xia Hu. 2024. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods. In The Twelfth International Conference on Learning Representations, ICLR 2024. https://openreview.net/forum?id=TzAJbTClAz

    2024

  2. [10]

    Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportu- nity in Supervised Learning. In Advances in Neural Information Process- ing Systems 29: Annual Conference on Neural Information Processing Sys- tems 2016 . 3315–3323. https://proceedings.neurips.cc/paper/201...

    2016

  3. [11]

    Hans Hofmann. 1994. Statlog (German Credit Data). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5NC77

    1994 doi

  4. [12]

    Faisal Kamiran and Toon Calders. 2011. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33, 1 (2011), 1–33. doi:10.1007/S10115-011-0463-8

    2011 doi

  5. [13]

    Faisal Kamiran, Asim Karim, and Xiangliang Zhang. 2012. Decision Theory for Discrimination-Aware Classification. In 12th IEEE International Conference on Data Mining, ICDM 2012 . 924–929. doi:10.1109/ICDM.2012.45

    2012 doi

  6. [14]

    Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012. Fairness-Aware Classifier with Prejudice Remover Regularizer. InMachine Learn- ing and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Vol. 7524. 35–50. doi:10.1007/978-3-642-33486-3_3

    2012 doi

  7. [15]

    Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu

    Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018 , Vol. 80. PMLR, 2569–2577. http://proceedi...

    2018

  8. [16]

    Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, and Feng Xia. 2024. FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 . 2072–2081. doi...

    2024

  9. [17]

    Rita P. Moro, S. and P. Cortez. 2014. Bank Marketing. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5K306

    2014 doi

  10. [18]

    Brodie Oldfield, Sevvandi Kandanaarachchi, Ziqi Xu, and Mario Andrés Muñoz

  11. [19]

    Kleinberg, and Kilian Q

    Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, and Kilian Q. Wein- berger. 2017. On Fairness and Calibration. In Advances in Neural Informa- tion Processing Systems 30: Annual Conference on Neural Information Process- ing Systems 2017. 5680–5689. https://proceeding...

    2017

  12. [20]

    ProPublica. 2016. ProPublica COMPAS Recidivism Dataset. https://github.com/ propublica/compas-analysis

    2016

  13. [21]

    Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar

    Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar. 2018. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfair- ness via Inequality Indices. In Proceedings of the 24th...

    2018

  14. [22]

    Hilde J. P. Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, and Michael Madaio. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems. Journal of Machine Learning Research 24 (2023), 257:1–257:8. https: //jmlr.org/papers/v24/23-0389.html

    2023

  15. [23]

    Ziqi Xu, Sevvandi Kandanaarachchi, Cheng Soon Ong, and Eirini Ntoutsi. 2025. Fairness Evaluation with Item Response Theory. In Proceedings of the ACM on Web Conference 2025, WWW 2025 . 2276–2288. doi:10.1145/3696410.3714883

    2025

  16. [24]

    Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, and Ke Wang. 2023. Disen- tangled Representation with Causal Constraints for Counterfactual Fairness. In Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, ...

    2023 doi

  17. [25]

    Ziqi Xu, Chenglong Ma, Yongli Ren, Jeffrey Chan, Wei Shao, and Feng Xia. 2025. Towards Better Evaluation of Recommendation Algorithms with Bi-directional Item Response Theory. In Companion Proceedings of the ACM on Web Conference 2025, WWW 2025 . 1455–1459. doi:10.1145/3701716.3715540

    2025

  18. [26]

    Zhenlong Xu, Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, and Ke Wang

  19. [27]

    Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork

    Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork

  20. [28]

    Sichen Zhao, Wei Shao, Jeffrey Chan, Ziqi Xu, and Flora Salim. 2025. FairDRL- ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction. arXiv preprint arXiv:2508.07518 (2025)

    2025 arXiv

  21. [2013]

    In Proceedings of the 30th International Conference on Machine Learning, ICML 2013 , Vol

    Learning Fair Representations. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013 , Vol. 28. 325–333. http://proceedings. mlr.press/v28/zemel13.html

    2013

  22. [2022]

    In Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Vol

    Assessing Classifier Fairness with Collider Bias. In Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Vol. 13281. 262–276. doi:10.1007/978-3-031-05936-0_21

    2022 doi

  23. [2025]

    SoftwareX 31 (2025), 102239

    An Item Response Theory-based R module for Algorithm Portfolio Analysis. SoftwareX 31 (2025), 102239. doi:10.1016/J.SOFTX.2025.102239

    2025

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