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

Author:

Tianyu Zhao, Salma Elmalaki

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computers and Society (cs.CY), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), Numerical Analysis (math.NA)

journal:

date:

2023-11-05 16:00:00

Abstract

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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