mlsanityData to 7 Oct 2026

fairlib: A Unified Framework for Assessing and Improving Classification Fairness

arXiv 2205.01876 ↗First posted 4 May 2022cs.LG cs.AI cs.CYcited by 4

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Abstract (arXiv)

This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing baseline models, developing new methods, evaluating models with different metrics, and visualizing their results. Its modularity and extensibility enable the framework to be used for diverse types of inputs, including natural language, images, and audio. In detail, we implement 14 debiasing methods, including pre-processing, at-training-time, and post-processing approaches. The built-in metrics cover the most commonly used fairness criterion and can be further generalized and customized for fairness evaluation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

22 of 38 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  5. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
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  10. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  11. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 194
  12. 2018Adversarial Removal of Demographic Attributes from Text Datacited by 166
10 more
  1. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  2. 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
  3. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  4. 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
  5. 2018Towards Robust and Privacy-preserving Text Representationscited by 75
  6. 2021Diverse Adversaries for Mitigating Bias in Trainingcited by 48
  7. 2020Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizationscited by 40
  8. 2021Contrastive Learning for Fair Representationscited by 34
  9. 2020Fairness with Overlapping Groupscited by 12
  10. 2022Towards Equal Opportunity Fairness through Adversarial Learningcited by 8

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Cited by year

Citing papers per year: 2022: 3; 2023: 1.

4 papers.

  1. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  2. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  3. 2023Erasure of Unaligned Attributes from Neural Representationscited by 6
  4. 2022Systematic Evaluation of Predictive Fairnesscited by 3