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

by   Tianyu Zhao, et al.

Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior and expectations are affected by the same adaptation decisions in the system. This human variability factor adds more complexity since policies deemed fair at one point in time may become discriminatory over time due to variations in human preferences resulting from inter- and intra-human variability. This paper addresses the fairness problem from an equity lens, considering human behavior variability, and the changes in human preferences over time. We propose FAIRO, a novel algorithm for fairness-aware sequential-decision making in HITL adaptation, which incorporates these notions into the decision-making process. In particular, FAIRO decomposes this complex fairness task into adaptive sub-tasks based on individual human preferences through leveraging the Options reinforcement learning framework. We design FAIRO to generalize to three types of HITL application setups that have the shared adaptation decision problem. Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility. To address this challenge, we provide a fairness-utility tradeoff in FAIRO, allowing system designers to balance the objectives of fairness and utility based on specific application requirements. Extensive evaluations of FAIRO on the three HITL applications demonstrate its generalizability and effectiveness in promoting fairness while accounting for human variability. On average, FAIRO can improve fairness compared with other methods across all three applications by 35.36


page 7

page 9

page 11

page 13


FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT

Thanks to the rapid growth in wearable technologies, monitoring complex ...

Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

Today, AI is increasingly being used in many high-stakes decision-making...

adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems

Reinforcement learning (RL) presents numerous benefits compared to rule-...

Achieving Long-Term Fairness in Sequential Decision Making

In this paper, we propose a framework for achieving long-term fair seque...

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility

When self-adaptive systems encounter changes within their surrounding en...

Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction

Recently, almost all conferences have moved to virtual mode due to the p...

Sequential Processing of Observations in Human Decision-Making Systems

In this work, we consider a binary hypothesis testing problem involving ...

Please sign up or login with your details

Forgot password? Click here to reset