Fairness control for risky artificial intelligence decision making

Abstract

When we do not know why algorithms make the decisions they do, this can lead to automatic decisions that are untrustworthy and unfair to minority groups. AI models are really good at giving us accurate decisions with respect to historical data sets with previous decisions. However, when we do not know the reasoning why a model is making its decisions, it is possible to be accurate while also being unfair. The author has developed an algorithm that is guaranteed to make the outputs of any AI decision making model fair, without having to fully know its inner workings. Fairness Adjusted Selective Inference, or FASI for short, can work with any AI model (without knowledge of its complicated inner processes) and select individuals for high-risk decisions with rigorous fairness control over the definitive decisions made across minority groups.