Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?

TL;DR

This paper critically evaluates whether clinicians are ethically obligated to disclose their use of medical ML systems to patients. It systematically analyzes four common arguments—risk-based, rights-based, materiality-based, and autonomy-based—and argues that each is unconvincing, offering reasons tied to safety governance, feasibility, empirical grounding, and the practical limits of disclosure. The author also highlights potential harms of a disclosure mandate, such as allowing stakeholders to dodge accountability for improper use. The work emphasizes the need for improved design, testing, regulatory oversight, and transparent governance to ensure ethical deployment of ML in medicine.

Abstract

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.