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

Regulatory compliance is now a practical necessity: International regulations like the AI Act require companies to actively measure data quality and detect bias in high-risk AI systems, making bias detection tools no longer optional but mandatory for responsible AI deployment.

Traditional bias detection methods have real limitations: Existing approaches for measuring bias (such as MMD and Wasserstein-1 distance methods) face significant scalability and computational challenges, which can make them impractical for real-world applications—a gap this toolkit aims to address.

New methods make bias detection more practical: The toolkit introduces two new approaches—maximum subgroup discrepancy (MSD) and subsampled ℓ∞ distances—specifically designed to overcome the computational barriers that have limited adoption of bias detection in practice.

Accessibility matters for responsible AI: By providing an easy-to-use Python toolkit with clear documentation and examples, the developers are lowering the barrier to entry for practitioners who want to detect bias, making responsible AI practices more achievable across organizations of different sizes and technical capabilities.

Open-source tools support industry-wide standards: Releasing the toolkit under the Apache License makes it freely available to the broader AI community, helping establish common standards and practices for bias detection across the industry.