Fairness in Learning-Based Sequential Decision Algorithms: A Survey
  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.
