The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

这篇研究论文提出了用新的风险法律方法来规范人工智能的应用，重点着眼于如何检测和减少人工智能中的偏差，并提出了一种基于N-Sigma统计学方法的新方法来测量机器学习模型的偏差，以及如何将其应用于开发新的风险评估框架，还探讨了该方法与其他流行统计测试方法的优缺点。

利用统计方法测量人工智能模型中的偏差及其应用于面部生物特征识别