一款可检测高风险AI模型偏见的开源工具包，解决传统方法计算难、扩展性差的问题。
humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models
- 采用新提出的最大子组差异和子采样无穷范数距离检测偏见
- 支持大规模数据集上的高效偏见评估，克服传统方法的计算瓶颈
- 适合关注AI可信性与合规性的研究人员与开发者
当前对可信AI的关注日益增强，国际法规如《人工智能法案》要求从业者评估高风险AI系统输入数据质量并估计输出偏见。然而，传统方法在测度空间距离估计上面临可扩展性（如MMD）和可计算性（如Wasserstein-1）难题。本文提出humancompatible.detect，一个针对偏见检测的Python工具包，集成两种新开发的方法：最大子组差异（MSD）与子采样ℓ∞距离。该工具包提供易用API，并附带多个使用示例。其代码遵循Apache许可证2.0发布。
原文摘要 · Abstract (English)
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 $\ell_\infty$ 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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