Discrimination in machine learning algorithms
This tackles ethical and legal issues in AI fairness for individuals affected by automated decisions, but it is incremental as it focuses on existing detection methods rather than introducing new solutions.
The paper addresses the problem of machine learning algorithms potentially discriminating based on sensitive attributes like sex or race in business decisions such as credit scoring, and it highlights the need for statistical tools to detect and eliminate these biases.
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.