Hammer or Measuring Tape? Artificial Intelligence and Justice in Healthcare

Abstract

Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI's extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin's <i>accountability for reasonableness</i> conception, needs amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create.

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