Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective
ACM Transactions on Management Information Systems, forthcoming.
29 Pages Posted: 10 Sep 2019 Last revised: 22 Feb 2022
Date Written: February 15, 2021
Many important decisions are increasingly being made with the help of information systems that use artificial intelligence and machine learning models. These computational models are designed to discover useful patterns from large amounts of data, which augment human capabilities to make decisions in various application domains. However, there are growing concerns regarding the ethics challenges faced by these automated decision-making (ADM) models, most notably on the issue of algorithmic bias, where the models systematically produce less favorable (i.e., unfair) decisions for certain groups of people. In this commentary, we argue that algorithmic bias is not just a technical (e.g., computational or statistical) problem, and its successful resolution requires deep insights into individual and organizational behavior, economic incentives, as well as complex dynamics of the sociotechnical systems in which the ADM models are embedded. We discuss a human-centric, fairness-aware ADM framework which highlights the holistic involvement of human decision makers in each step of ADM. We review the emerging literature on fairness-aware machine learning, and then discuss various strategic decisions that humans need to make, such as formulating proper fairness objectives, recognizing fairness-induced trade-offs and implications, utilizing machine learning model outputs, and managing/governing the decisions of ADM models. We further illustrate how these strategic decisions are jointly informed by behavioral, economic, and design sciences. Our discussions reveal a number of future research opportunities uniquely suitable for Management Information Systems (MIS) researchers to pursue.
Keywords: automated decision-making, algorithmic bias, ethics, fairness, augmented decision-making
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