  Algorithmic decision-making in societal contexts, such as retail pricing,
loan administration, recommendations on online platforms, etc., often involves
experimentation with decisions for the sake of learning, which results in
perceptions of unfairness among people impacted by these decisions. It is hence
necessary to embed appropriate notions of fairness in such decision-making
processes. The goal of this paper is to highlight the rich interface between
temporal notions of fairness and online decision-making through a novel
meta-objective of ensuring fairness at the time of decision. Given some
arbitrary comparative fairness notion for static decision-making (e.g.,
students should pay at most 90% of the general adult price), a corresponding
online decision-making algorithm satisfies fairness at the time of decision if
the said notion of fairness is satisfied for any entity receiving a decision in
comparison to all the past decisions. We show that this basic requirement
introduces new methodological challenges in online decision-making. We
illustrate the novel approaches necessary to address these challenges in the
context of stochastic convex optimization with bandit feedback under a
comparative fairness constraint that imposes lower bounds on the decisions
received by entities depending on the decisions received by everyone in the
past. The paper showcases novel research opportunities in online
decision-making stemming from temporal fairness concerns.
