mlsanityData to 14 Oct 2026

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models

arXiv 2509.24340 ↗First posted 29 Sep 2025cs.AIcited by 0

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Abstract (arXiv)

There is a strong recent emphasis on trustworthy AI. In particular, international regulations, such as the AI Act, demand that AI practitioners measure data quality on the input and estimate bias on the output of high-risk AI systems. However, there are many challenges involved, including scalability (MMD) and computability (Wasserstein-1) issues of traditional methods for estimating distances on measure spaces. Here, we present humancompatible.detect, a toolkit for bias detection that addresses these challenges. It incorporates two newly developed methods to detect and evaluate bias: maximum subgroup discrepancy (MSD) and subsampled ℓ_∞ distances. It has an easy-to-use API documented with multiple examples. humancompatible.detect is licensed under the Apache License, Version 2.0.

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References · 17

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
  3. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  4. 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
  5. 2023Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuringcited by 53
  6. 2024MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenizationcited by 31
  7. 2017Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigationcited by 24
  8. 2019FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparencycited by 11
  9. 2023Computability of Optimizerscited by 5
  10. 2025Adultification Bias in LLMs and Text-to-Image Modelscited by 5
  11. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  12. 2022Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactionscited by 3
5 more
  1. 2025Bias Detection via Maximum Subgroup Discrepancycited by 2
  2. 2023When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendationscited by 1
  3. 2024Fairness in Ranking: Robustness through Randomization without the Protected Attributecited by 1
  4. 2025Sample Complexity of Bias Detection with Subsampled Point-to-Subspace Distancescited by 1
  5. 2025Generalizing while preserving monotonicity in comparison-based preference learning modelscited by 1

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