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.

本文研究了日常决策过程中机器学习技术的逐渐应用所带来的公正性问题，特别关注碰撞器偏差问题，并提出了使用定理指导公平评估来减少碰撞器偏差的无偏评估算法。实验和模拟表明，该算法可以显著减少评估时的碰撞器偏差，并在审计训练好的分类器中表现良好。