Work

humancompatible.detect: a Python toolkit for detecting bias in AI models

Year: 2026

Type: software-paper

Abstract: 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 in 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 features an easy-to-use API documented with multiple examples. humancompatible.detect is licensed under the Apache License, Version 2.0.

Source: SoftwareX

DOI: 10.1016/j.softx.2026.102827

Authors: Germán M. Matilla, Jiří Němeček, Illia Kryvoviaz, Jakub Mareček

Institution: Czech Technical University in Prague

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,200