Fairness in Learning-Based Sequential Decision Algorithms: A Survey
It addresses fairness in sequential decision algorithms, which is crucial for real-world applications where decisions impact users over time, but as a survey, it is incremental in synthesizing existing work.
This survey reviews literature on fairness in data-driven sequential decision-making, distinguishing between cases where past decisions do or do not affect future data, and examines the impact of fairness interventions on populations.
Algorithmic fairness in decision-making has been studied extensively in static settings where one-shot decisions are made on tasks such as classification. However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data. This is particularly the case when decisions affect the individuals or users generating the data used for future decisions. In this survey, we review existing literature on the fairness of data-driven sequential decision-making. We will focus on two types of sequential decisions: (1) past decisions have no impact on the underlying user population and thus no impact on future data; (2) past decisions have an impact on the underlying user population and therefore the future data, which can then impact future decisions. In each case the impact of various fairness interventions on the underlying population is examined.