BiMi Sheets: Infosheets for bias mitigation methods

Guillaume Bied; Jefrey Lijffijt; Maarten Buyl; MaryBeth Defrance; Tijl De Bie

REVIEW 2 major objections 5 minor 44 references

BiMi Sheets: Infosheets for bias mitigation methods

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read One infosheet to document any bias mitigation method

desk verdict BiMi Sheets is a useful documentation standard for bias-mitigation methods, but the two 'Grid Search Reduction' example sheets contradict each other on the load-bearing Fairness Guarantee label, so the uniformity claim needs real reliability evidence before it can be trusted. read the letter →

arxiv 2505.22114 v1 pith:AQ2XW7WI submitted 2025-05-28 cs.LG

MaryBeth Defrance , Guillaume Bied , Maarten Buyl , Jefrey Lijffijt , Tijl De Bie This is my paper

classification cs.LG

keywords biasmitigationfairnessdocumentationmodelcardsdatasheetsportabilitytrapdebtfairmachinelearninginfosheets

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

This paper argues that bias mitigation methods in machine learning suffer from documentation debt: their design choices are scattered across APIs, user guides, and notebooks, or left implicit, making methods hard to compare and hard for practitioners to adopt. It proposes BiMi Sheets, a portable and uniform template of six sections that document a method's description, pipeline location, fairness formalization, implementation constraints, tested use cases, and metadata. If adopted, the sheets would give researchers a common language for benchmarking and practitioners a structured way to find methods that fit their task and pipeline. The paper provides 24 example sheets for popular methods and a public platform to host and search them.

What carries the argument

The central object is the BiMi Sheet itself: a six-section, label-plus-free-text document. The labels are the machinery: they come from a synthesis of existing fairness taxonomies (method types, ML tasks, pipeline locations, fairness types and definitions, sensitive attribute compositions, fairness guarantees), and they are what makes methods comparable across papers and toolkits. Free text in each section preserves the contextual detail that the portability trap requires. The paper also provides a platform (bimisheet.com) with 24 filled sheets to bootstrap adoption.

What would settle it

Take a recent bias mitigation method and try to complete a BiMi Sheet for it. If the method's key design choices cannot be recorded without adding new labels or leaving important axes blank, the portability claim fails. Alternatively, a user study comparing how well practitioners find suitable methods with and without the sheets would put the practical claim to a direct test.

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Extended reading notes

Core claim

The central claim is that a fixed set of labels, drawn from existing AI fairness taxonomies, can capture the design choices of any bias mitigation method well enough to be useful. Each BiMi Sheet records the method type, ML task, compatible dataset types, pipeline location, compatible models, fairness type and definition, composition of sensitive attributes, fairness guarantee, implementation constraints, and tested use cases, alongside free-text descriptions. The paper shows that current online documentation of five major fairness toolkits is not uniform in where or whether these characteristics are reported, and argues that BiMi Sheets fill this gap.

Load-bearing premise

The load-bearing premise is that a fixed set of short labels can capture the design choices of every bias mitigation method well enough to make methods comparable; the paper itself notes fairness is domain-, task-, and model-specific.

Editorial extensions

If this is right

Researchers can benchmark new methods against all prior methods whose design choices align, not just a fixed 2016–2019 baseline set.
Practitioners can quickly identify whether a method supports their ML task, dataset type, pipeline stage, and sensitive attribute composition.
A structured database of sheets enables searchable comparison across hundreds of methods.
Fairness toolkits could embed BiMi Sheets, improving the findability of the methods they provide.
The label structure can evolve as new fairness notions and method types emerge.

Reading between the lines

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

The same sheet pattern could be adapted to other ML subfields with similar documentation debt, such as explainability or privacy-enhancing methods.
If sheets became a submission standard, they could reduce redundant reimplementation and misbenchmarking in fairness research.
A possible testable extension is to derive a machine-readable schema that lets platforms auto-match methods to a practitioner's stated constraints.
The sheets' value depends on brand-neutral maintenance; a community governance model would matter more than the template itself.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague — the useful thing here is the artifact: a six-section documentation schema, plus 24 filled sheets and a searchable platform, for bias mitigation methods rather than datasets, models, or services. That fills a real gap. Model Cards don't cover mitigation methods, and the paper's documentation-debt audit (Table 1) shows how scattered existing toolkit docs are. The schema is sensibly derived from published survey taxonomies, and the 'portability trap' motivation is genuine. The sheets themselves are mostly informative, and the concrete implementation-constraint fields are practically useful.

The soft spots are real but not fatal. The central claim — that this is a portable, uniform guide letting users quickly compare methods — depends on people applying the labels consistently. The paper gives no inter-annotator agreement evidence, and its own appendix contradicts the claim. Two sheets both named 'Grid Search Reduction' from AIF360 carry opposite Fairness Guarantee tags: the first says 'Fairness Guaranteed' and actually describes Exponentiated Gradient reduction; the second says 'No Fairness Guarantee' for the deterministic grid search. The first sheet also mislabels its method description. That is a copy-paste error, and it is load-bearing because if the authors cannot keep their own examples consistent, the claim that a fixed label set makes methods comparable is exactly what needs evidence. The 'Ongoing challenges' section anticipates adoption issues and label evolution but not this more basic reliability problem.

There is no user study; the claimed benefit to practitioners is plausible but unmeasured. That is a normal limitation for a proposal paper, not a reason to reject. The self-citations to fairret and ABCFair are fine — those are real external artifacts. I disagree mildly with the reader's framing: the stress-test concern lands squarely, and I would make reliability the primary revision demand, not the expressiveness worry about fixed labels.

Who this is for: fair-ML researchers benchmarking methods, practitioners choosing a mitigation method, and people designing documentation standards. It deserves a serious referee and a conditional-accept path. I would want the authors to (1) fix the Grid Search Reduction sheets, (2) run a small inter-annotator study where different people fill the same sheet for the same method, and (3) say explicitly what would count as validation of the uniformity claim. The schema is worth building on even if BiMi Sheets is not adopted wholesale.

Referee Report

2 major / 5 minor

Summary. The paper proposes BiMi Sheets, a documentation schema for bias mitigation methods consisting of six sections: metadata, method description, pipeline architecture, fairness type, implementation constraints, and tested use cases. The schema mixes structured labels with free text, and the authors provide 24 example sheets for methods from AIF360, Fairlearn, error-parity, fairret, and OxonFair, together with an open-source platform at bimisheet.com. The central claim is that BiMi Sheets form a portable, uniform guide that enables researchers and practitioners to quickly learn a method's main characteristics and compare methods against their desiderata, thereby reducing documentation debt in fair ML and supporting structured benchmarking.

Significance. The paper addresses a real and recognized problem: bias mitigation methods are documented inconsistently across toolkits, and the documentation-availability analysis in Table 1 supports this. The proposal builds sensibly on prior documentation initiatives (Datasheets, Model Cards, FactSheets) and on fairness surveys, and the shipping of 24 example sheets plus an open platform is a concrete, reproducible contribution. If the schema were adopted and labels were applied consistently, it could indeed help practitioners and researchers navigate the method space. However, the paper's own example sheets contain a direct contradiction in label assignment, and no evidence is provided that different documenters would fill in the sheets consistently. These issues undermine the central uniformity claim as currently presented.

major comments (2)

[BiMi Sheet examples (Appendix)] The two sheets titled 'Grid Search Reduction' for AIF360 v0.6.1 assign contradictory Fairness Guarantee labels: the first sheet (whose Method Description actually describes Exponentiated Gradient Reduction) states 'Fairness Guaranteed', while the later sheet states 'No Fairness Guarantee'. Because the paper's core claim is that the tag fields provide a trustworthy basis for comparing methods, this internal inconsistency is not merely typographical: a user comparing methods through these tags would receive opposite answers for the same method. The sheets should be corrected, and the schema should clarify whether 'Fairness Guarantee' refers to a theoretical property of the algorithm, to the behavior of the specific implementation, or to the feasibility of satisfying the constraint in practice.
[Structure of the sheets; Ongoing challenges] The central claim that BiMi Sheets enable users to 'quickly learn' and compare bias mitigation methods is asserted but not evaluated. The paper presents no user study, no inter-annotator reliability analysis, and no demonstration that the label vocabulary is sufficient or unambiguous in practice. Given that the uniformity and portability claims are the main contribution, a small inter-annotator study (for example, having independent annotators fill in a subset of sheets and measuring agreement on the categorical labels) would substantiate the framework; without such evidence, the claim remains an unsupported assertion about usability.

minor comments (5)

[BiMi Sheet examples (Appendix), first Grid Search Reduction sheet] The sentence 'Users are required to provide a classifier or regression model satisfying scikit-learn conventions for the ’fit’ and ’predict’ methods (wth 0-1 outputs for ’predict’)' contains a typo: 'wth' should be 'with'.
[Table 1] The column header 'CompositionSensitiveAttributes' is missing a space; it should read 'Composition Sensitive Attributes'.
[BiMi Sheet examples (Appendix), Disparate Impact Remover sheet] The Fairness Description in the Disparate Impact Remover sheet ends with the incomplete sentence 'This follows', which should be completed or removed.
[Ongoing challenges] The sentence 'the robustness of attempting to partially standardize labels describing bias limitation methods' uses 'bias limitation methods' where 'bias mitigation methods' is intended.
[Abstract] The phrase 'the sheets' structure allow for the creation' is a subject-verb agreement error and should read 'allows'.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the sheet structure is an organizational artifact built from external taxonomies, with only minor non-load-bearing self-citations.

full rationale

The paper proposes a documentation template ('BiMi Sheets') for bias mitigation methods; it contains no mathematical derivation, fitted parameter, or quantity predicted from inputs, so the standard circularity patterns (self-definitional, fitted-input-called-prediction, ansatz-smuggled-in) do not apply. The central claim — that the sheets enable quick learning and comparison — is supported by the structure itself plus 24 filled-in example sheets, which are external artifacts (AIF360, Fairlearn, error-parity, fairret, OxonFair) rather than outputs of the paper's own argument. The label vocabulary is explicitly imported from prior surveys ('We rename the labels from these taxonomies to Method Type'; 'BiMi Sheets build on these common elements and on domain-specific taxonomies'), and the paper credits those surveys, so the organizing vocabulary is externally sourced, not derived from its own conclusion. The authors cite their own prior work in three places: ABCFair (Defrance, Buyl, and Bie 2024) supports the motivational claim that design choices must be alignable for comparison, the pipeline-location sentence leans on the same citation, and fairret (Buyl, Defrance, and Bie 2024) is cited in Table 1, with two fairret sheets included as examples. These citations are minor and non-load-bearing: they provide background motivation and example artifacts, while the sheets' content (labels, method descriptions, fairness definitions) is drawn from the cited methods' own documentation and from independent surveys. No step reduces to its own input; the only genuine concern evidenced in the paper is reliability of label application (the two 'Grid Search Reduction' sheets assign opposite 'Fairness Guarantee' values and one mislabels its description as Exponentiated Gradient), which is a consistency and validation problem for the framework's practical value, not a circularity. Score 1 reflects minor self-citation without load-bearing circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 1 invented entities

The paper does not fit free parameters or invent physical entities. It assumes that a fixed label set can describe diverse mitigation methods and that documentation debt is a real obstacle; both are reasonable but unproven. The only self-introduced artifact is the BiMi Sheets schema itself.

assumptions (2)

domain assumption A fixed set of labels can span all bias mitigation methods
The paper assumes the six-section schema with the listed labels suffices to describe any method across domains, despite the 'portability trap' motivating the work. Invoked in Section 'Structure of the sheets'.
domain assumption Documentation debt exists and hinders practitioners
The motivation relies on prior user studies (Deng et al. 2022; Lee and Singh 2021) and the authors' own audit of five toolkits in Table 1; this is a background assumption, not independently proven here.

invented entities (1)

BiMi Sheets schema
purpose: Standardized documentation format for bias mitigation methods
No external validation or falsifiable prediction; its utility is asserted through 24 self-made example sheets and a website, with no independent evaluation.

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Cite this review

Pith. "Pith review of BiMi Sheets: Infosheets for bias mitigation methods." pith.science (2026). https://pith.science/paper/AQ2XW7WI

@misc{pith2026250522114,
  author       = {Pith},
  title        = {Pith review of: BiMi Sheets: Infosheets for bias mitigation methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AQ2XW7WI}},
  note         = {Machine review of arXiv:2505.22114}
}

read the original abstract

Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-, and model-specific, leading to a `portability trap': bias mitigation solutions in one context may not be appropriate in another. Thus, a myriad of design choices have to be made when creating a bias mitigation method, such as the formalization of fairness it pursues, and where and how it intervenes in the ML pipeline. This creates challenges in benchmarking and comparing the relative merits of different bias mitigation methods, and limits their uptake by practitioners. We propose BiMi Sheets as a portable, uniform guide to document the design choices of any bias mitigation method. This enables researchers and practitioners to quickly learn its main characteristics and to compare with their desiderata. Furthermore, the sheets' structure allow for the creation of a structured database of bias mitigation methods. In order to foster the sheets' adoption, we provide a platform for finding and creating BiMi Sheets at bimisheet.com.

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Works this paper leans on

44 extracted references · 31 canonical work pages

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Arnold, M.; Bellamy, R. K. E.; Hind, M.; Houde, S.; Mehta, S.; Mojsilovic, A.; Nair, R.; Ramamurthy, K. N.; Reimer, D.; Olteanu, A.; Piorkowski, D.; Tsay, J.; and Varshney, K. R. 2019. FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity. (arXiv:1808.07261). ArXiv:1808.07261 [cs]

arXiv 2019
[4]

Bartl, M.; Mandal, A.; Leavy, S.; and Little, S. 2024. Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey. ACM Comput. Surv. Just Accepted

work page 2024
[5]

Bellamy, R. K. E.; Dey, K.; Hind, M.; Hoffman, S. C.; Houde, S.; Kannan, K.; Lohia, P.; Martino, J.; Mehta, S.; Mojsilovic, A.; Nagar, S.; Ramamurthy, K. N.; Richards, J.; Saha, D.; Sattigeri, P.; Singh, M.; Varshney, K. R.; and Zhang, Y. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

work page 2018
[6]

Buolamwini, J.; and Gebru, T. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91. PMLR

work page 2018
[7]

Buyl, M.; and De Bie, T. 2024. Inherent limitations of AI fairness. Communications of the ACM, 67(2): 48--55

work page 2024
[8]

Buyl, M.; Defrance, M.; and Bie, T. D. 2024. fairret: a Framework for Differentiable Fairness Regularization Terms. (arXiv:2310.17256). ArXiv:2310.17256 [cs]

arXiv 2024

Show all 44 references

[9]

Caton, S.; and Haas, C. 2024. Fairness in Machine Learning: A Survey. ACM Computing Surveys, 56(7): 1–38

2024
[10]

Chu, Z.; Wang, Z.; and Zhang, W. 2024. Fairness in Large Language Models: A Taxonomic Survey. ACM SIGKDD Explorations Newsletter, 26(1): 34–48

2024
[11]

Cruz, A.; and Hardt, M. 2024. Unprocessing Seven Years of Algorithmic Fairness. In The Twelfth International Conference on Learning Representations

2024
[12]

Defrance, M.; Buyl, M.; and Bie, T. D. 2024. ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods. (arXiv:2409.16965). ArXiv:2409.16965 [cs]

2024 arXiv
[13]

Delaney, E.; Fu, Z.; Wachter, S.; Mittelstadt, B.; and Russell, C. 2024. OxonFair: A Flexible Toolkit for Algorithmic Fairness. (arXiv:2407.13710). ArXiv:2407.13710 [cs]

2024 arXiv
[14]

H.; Nagireddy, M.; Lee, M

Deng, W. H.; Nagireddy, M.; Lee, M. S. A.; Singh, J.; Wu, Z. S.; Holstein, K.; and Zhu, H. 2022. Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT '22, 473...

2022
[15]

Developers, T. 2024. TensorFlow

2024
[16]

Ding, F.; Hardt, M.; Miller, J.; and Schmidt, L. 2024. Retiring adult: new datasets for fair machine learning. In Proceedings of the 35th International Conference on Neural Information Processing Systems, NIPS '21. Red Hook, NY, USA: Curran Associates Inc. ISBN 9781713845393

2024
[17]

d’Alessandro, B.; O’Neil, C.; and LaGatta, T. 2017. Conscientious Classification: A Data Scientist’s Guide to Discrimination-Aware Classification. Big Data, 5(2): 120–134. ArXiv:1907.09013 [stat]

2017 arXiv
[18]

D.; Das, A.; Burke, R.; Diaz, F.; et al

Ekstrand, M. D.; Das, A.; Burke, R.; Diaz, F.; et al. 2022. Fairness in information access systems. Foundations and Trends in Information Retrieval , 16(1-2): 1--177

2022
[19]

Fabris, A.; Messina, S.; Silvello, G.; and Susto, G. A. 2022. Algorithmic fairness datasets: the story so far. Data Mining and Knowledge Discovery, 36(6): 2074–2152

2022
[20]

Face, H. 2025. The AI community building the future

2025
[21]

O.; Rossi, R

Gallegos, I. O.; Rossi, R. A.; Barrow, J.; Tanjim, M. M.; Kim, S.; Dernoncourt, F.; Yu, T.; Zhang, R.; and Ahmed, N. K. 2024. Bias and Fairness in Large Language Models: A Survey. Computational Linguistics, 50(3): 1097–1179

2024
[22]

W.; Wallach, H.; III, H

Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; III, H. D.; and Crawford, K. 2021. Datasheets for Datasets. (arXiv:1803.09010). ArXiv:1803.09010 [cs]

2021 arXiv
[23]

Green, B. 2019. “Good” isn’t good enough

2019
[24]

Gursoy, F.; and Kakadiaris, I. A. 2022. System Cards for AI-Based Decision-Making for Public Policy. (arXiv:2203.04754). ArXiv:2203.04754 [cs]

2022 arXiv
[25]

Han, X.; Chi, J.; Chen, Y.; Wang, Q.; Zhao, H.; Zou, N.; and Hu, X. 2024. FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods. (arXiv:2306.09468). ArXiv:2306.09468 [cs]

2024 arXiv
[26]

John-Mathews, J.-M.; Cardon, D.; and Balagu \'e , C. 2022. From reality to world. A critical perspective on AI fairness. Journal of Business Ethics, 178(4): 945--959

2022
[27]

Laclau, C.; Largeron, C.; and Choudhary, M. 2024. A Survey on Fairness for Machine Learning on Graphs. (arXiv:2205.05396). ArXiv:2205.05396 [cs]

2024 arXiv
[28]

Lee, M. S. A.; and Singh, J. 2021. The Landscape and Gaps in Open Source Fairness Toolkits. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21. New York, NY, USA: Association for Computing Machinery. ISBN 9781450380966

2021
[29]

Li, Y.; Chen, H.; Xu, S.; Ge, Y.; Tan, J.; Liu, S.; and Zhang, Y. 2023. Fairness in recommendation: Foundations, methods, and applications. ACM Transactions on Intelligent Systems and Technology, 14(5): 1--48

2023
[30]

Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021. A Survey on Bias and Fairness in Machine Learning. ACM Comput. Surv., 54(6): 115:1--115:35

2021
[31]

D.; and Gebru, T

Mitchell, M.; Wu, S.; Zaldivar, A.; Barnes, P.; Vasserman, L.; Hutchinson, B.; Spitzer, E.; Raji, I. D.; and Gebru, T. 2019. Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* ’19, 220–229. New York, NY, USA: A...

2019
[32]

P.; Loureiro, R

Pagano, T. P.; Loureiro, R. B.; Lisboa, F. V. N.; Peixoto, R. M.; Guimarães, G. A. S.; Cruz, G. O. R.; Araujo, M. M.; Santos, L. L.; Cruz, M. A. S.; Oliveira, E. L. S.; Winkler, I.; and Nascimento, E. G. S. 2023. Bias and Unfairness in Machine Learning Models: A Systematic Rev...

2023
[33]

pandas development team, T. 2020. pandas-dev/pandas: Pandas

2020
[34]

Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019. PyTorch: An ...

2019
[35]

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; and Duchesnay, E. 2011. Scikit-learn: Machine Learning in P ython. Journ...

2011
[36]

F.; Thom, J.; and Cramer, H

Richardson, B.; Garcia-Gathright, J.; Way, S. F.; Thom, J.; and Cramer, H. 2021. Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21. New York, NY...

2021
[37]

T.; and Ghani, R

Saleiro, P.; Kuester, B.; Hinkson, L.; London, J.; Stevens, A.; Anisfeld, A.; Rodolfa, K. T.; and Ghani, R. 2019. Aequitas: A Bias and Fairness Audit Toolkit. (arXiv:1811.05577). ArXiv:1811.05577 [cs]

2019 arXiv
[38]

D.; Boyd, D.; Friedler, S

Selbst, A. D.; Boyd, D.; Friedler, S. A.; Venkatasubramanian, S.; and Vertesi, J. 2019. Fairness and Abstraction in Sociotechnical Systems. FAT* ’19, 59–68. New York, NY, USA: Association for Computing Machinery. ISBN 978-1-4503-6125-5

2019
[39]

Staufer, L.; Yang, M.; Reuel, A.; and Casper, S. 2025. Audit Cards: Contextualizing AI Evaluations. (arXiv:2504.13839). ArXiv:2504.13839 [cs]

2025 arXiv
[40]

Sylvester, J.; and Raff, E. 2018. What About Applied Fairness? ArXiv

2018
[41]

Wachter, S.; Mittelstadt, B.; and Russell, C. 2021. Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review, 41: 105567

2021
[42]

Wang, A.; Hertzmann, A.; and Russakovsky, O. 2024. Benchmark suites instead of leaderboards for evaluating AI fairness. Patterns, 5(11)

2024
[43]

Weerts, H.; Dudík, M.; Edgar, R.; Jalali, A.; Lutz, R.; and Madaio, M. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems

2023
[44]

Weerts, H.; Pfisterer, F.; Feurer, M.; Eggensperger, K.; Bergman, E.; Awad, N.; Vanschoren, J.; Pechenizkiy, M.; Bischl, B.; and Hutter, F. 2024. Can fairness be automated? Guidelines and opportunities for fairness-aware AutoML. Journal of Artificial Intelligence Research, 79:...

2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

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[3] [3]

Arnold, M.; Bellamy, R. K. E.; Hind, M.; Houde, S.; Mehta, S.; Mojsilovic, A.; Nair, R.; Ramamurthy, K. N.; Reimer, D.; Olteanu, A.; Piorkowski, D.; Tsay, J.; and Varshney, K. R. 2019. FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity. (arXiv:1808.07261). ArXiv:1808.07261 [cs]

arXiv 2019

[4] [4]

Bartl, M.; Mandal, A.; Leavy, S.; and Little, S. 2024. Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey. ACM Comput. Surv. Just Accepted

work page 2024

[5] [5]

Bellamy, R. K. E.; Dey, K.; Hind, M.; Hoffman, S. C.; Houde, S.; Kannan, K.; Lohia, P.; Martino, J.; Mehta, S.; Mojsilovic, A.; Nagar, S.; Ramamurthy, K. N.; Richards, J.; Saha, D.; Sattigeri, P.; Singh, M.; Varshney, K. R.; and Zhang, Y. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

work page 2018

[6] [6]

Buolamwini, J.; and Gebru, T. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91. PMLR

work page 2018

[7] [7]

Buyl, M.; and De Bie, T. 2024. Inherent limitations of AI fairness. Communications of the ACM, 67(2): 48--55

work page 2024

[8] [8]

Buyl, M.; Defrance, M.; and Bie, T. D. 2024. fairret: a Framework for Differentiable Fairness Regularization Terms. (arXiv:2310.17256). ArXiv:2310.17256 [cs]

arXiv 2024