论文标题
机器学习算法中的歧视
Discrimination in machine learning algorithms
论文作者
论文摘要
机器学习算法通常用于可能直接影响个人的业务决策，因为信用评分算法拒绝了他们的贷款。从道德（和法律）的角度来看，它是相关的，以确保这些算法不会基于敏感属性（例如性别或种族）来区分这些算法，而操作员和管理人员可能会在不知不觉中和不知情地发生。然后需要统计工具和方法来检测和消除这种潜在偏见。
Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.
