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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%.
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FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System
Ensuring fairness in decision-making systems within Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when diverse individuals, each with varying behaviors and expectations, coexist within the same application space, influenced by a shared set of control actions in the system. The long-term adverse effects of these actions further pose the challenge, as historical experiences and interactions shape individual perceptions of fairness. This paper addresses the challenge of fairness from an equity perspective of adverse effects, taking into account the dynamic nature of human behavior and evolving preferences while recognizing the lasting impact of adverse effects. We formally introduce the concept of Fairness-in-Adverse-Effects (FinA) within the HCPS context. We put forth a comprehensive set of five formulations for FinA, encompassing both the instantaneous and long-term aspects of adverse effects. To empirically validate the effectiveness of our FinA approach, we conducted an evaluation within the domain of smart homes, a pertinent HCPS application. The outcomes of our evaluation demonstrate that the adoption of FinA significantly enhances the overall perception of fairness among individuals, yielding an average improvement of 66.7% when compared to the state-of-the-art method.
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- PAR ID:
- 10537344
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6927-4
- Page Range / eLocation ID:
- 202 to 211
- Subject(s) / Keyword(s):
- human-cyber-physical-systems fairness decision-making adverse-effect
- Format(s):
- Medium: X
- Location:
- Hong Kong, Hong Kong
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCPS61052.2024.00025
