Algorithmic Bias from the Perspectives of Healthcare Professionals

Jennifer Xu; Tamara Babaian

cs.AIOct 27, 2026

Algorithmic Bias from the Perspectives of Healthcare Professionals

Authors: Jennifer Xu, Tamara Babaian

Organizations: Department of Computer Information Systems, Bentley University, Waltham, MA, U.S.A.

Abstract

This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adoption of ML algorithms and algorithm generated outcomes. The results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the algorithm characteristics. More research is needed to investigate algorithmic bias to ensure fairness and equality in healthcare.

Algorithmic Bias

Algorithmic bias studies how automated systems produce or amplify unfair outcomes across demographic groups, and how to measure and reduce them.

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AI Model Design and Physician Liability

Shujie Luan, Shubhranshu Singh, Tinglong Dai

The University of Western Australia, UWA Business School, Perth, Australia · Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202

Human-AI Decision Making Algorithmic Fairness Group Fairness Clinical Decision-Making

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Clinical algorithms can perform differently across patient groups, a concern that grows as these algorithms increasingly take the form of artificial intelligence (AI) models. In the United States, a 2024 federal rule bars healthcare providers that receive federal funds from discriminating through their use of clinical algorithms, which exposes them to liability when such use disadvantages a protected patient group. We examine how such liability considerations reshape (i) an AI firm's model design decisions that drive group-specific accuracy and (ii) a physician's decisions to use AI in healthcare delivery. The firm designs an AI model for two patient groups, and improving accuracy for the disadvantaged group is more costly. The physician (who remains the accountable decision maker) then decides whether to consult AI, weighing the reduction in clinical uncertainty against expected liability exposure when AI errors disproportionately affect the disadvantaged group. We find that this liability can induce disparate use of AI: the physician may reduce AI use overall and, over an intermediate range of liability, rely on AI less for disadvantaged patients. The effect is non-monotone. As liability increases, the physician's use of AI for disadvantaged patients first declines, then rises as the firm reallocates investment toward reducing disparity or switches to an equal-accuracy design. Mandating equal algorithmic accuracy across patient groups can then inadvertently harm both groups, because a uniform accuracy requirement distorts the firm's investment incentives and the physician's equilibrium AI-use decisions.

Abstract

Algorithmic Bias

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