Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>

TLDR

Machine‑learning algorithms are increasingly used in medicine, driven by large datasets, computing power, and improved predictive accuracy, yet their black‑box nature raises concerns about explainability and clinicians’ moral responsibilities. The author contends that opaque medical decisions are actually more prevalent than critics assume.

Abstract

Although decision-making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access "the knowledge within the machine." "Without an explanation in terms of reasons or a rationale for particular decisions in individual cases, some commentators regard ceding medical decision-making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious-as it often is in medicine-the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy.