Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
This work addresses ethical guidelines for clinicians and policymakers in healthcare AI, presenting a counterargument to established views, though it is incremental as it critiques existing ethical frameworks without introducing new empirical data or methods.
The paper challenges the ethical obligation for clinicians to disclose their use of medical machine learning systems to patients, arguing that four common arguments supporting this thesis are unconvincing and that mandating disclosure could harm patients by allowing stakeholders to avoid accountability for improper use.
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.