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FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System

arXiv 2311.03468 ↗First posted 6 Nov 2023cs.AI cs.CY cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 6 Nov 2023.

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22 of 46 references.

References · 22

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
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10 more
  1. 2020Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewardscited by 60
  2. 2019Causal Modeling for Fairness in Dynamical Systemscited by 46
  3. 2019Learning Fairness in Multi-Agent Systemscited by 44
  4. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  5. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
  6. 2018Downstream Effects of Affirmative Actioncited by 34
  7. 2021FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoTcited by 15
  8. 2018Adaptive Incentive Designcited by 6
  9. 2023adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systemscited by 6
  10. 2016Self-Awareness of Cloud Applicationscited by 1

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