论文标题
用对撞机偏见评估分类器公平
Assessing Classifier Fairness with Collider Bias
论文作者
论文摘要
机器学习技术在日常决策过程中的越来越多，引起了人们对算法决策的公平性的担忧。本文涉及对撞机偏见的问题，该问题在公平评估方面产生虚假的关联，并开发定理以指导公平评估避免对撞机偏见。我们考虑了由审计机构审核训练的分类器的现实应用。我们通过利用开发的定理来减少评估中的对撞机偏见，提出了一种公正的评估算法。实验和模拟表明，所提出的算法在评估中显着降低了对撞机的偏见，并且有望在审核训练的分类器中。
The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making. This paper concerns the problem of collider bias which produces spurious associations in fairness assessment and develops theorems to guide fairness assessment avoiding the collider bias. We consider a real-world application of auditing a trained classifier by an audit agency. We propose an unbiased assessment algorithm by utilising the developed theorems to reduce collider biases in the assessment. Experiments and simulations show the proposed algorithm reduces collider biases significantly in the assessment and is promising in auditing trained classifiers.
