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Achieving fairness in sequential-decision making systems withinHuman-in-the-Loop (HITL) environments is a critical concern, especially whenmultiple humans with different behavior and expectations are affected by thesame adaptation decisions in the system. This human variability factor addsmore complexity since policies deemed fair at one point in time may becomediscriminatory over time due to variations in human preferences resulting frominter- and intra-human variability. This paper addresses the fairness problemfrom an equity lens, considering human behavior variability, and the changes inhuman preferences over time. We propose FAIRO, a novel algorithm forfairness-aware sequential-decision making in HITL adaptation, whichincorporates these notions into the decision-making process. In particular,FAIRO decomposes this complex fairness task into adaptive sub-tasks based onindividual human preferences through leveraging the Options reinforcementlearning framework. We design FAIRO to generalize to three types of HITLapplication setups that have the shared adaptation decision problem.Furthermore, we recognize that fairness-aware policies can sometimes conflictwith the application's utility. To address this challenge, we provide afairness-utility tradeoff in FAIRO, allowing system designers to balance theobjectives of fairness and utility based on specific application requirements.Extensive evaluations of FAIRO on the three HITL applications demonstrate itsgeneralizability and effectiveness in promoting fairness while accounting forhuman variability. On average, FAIRO can improve fairness compared with othermethods across all three applications by 35.36%.

FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems