Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
arXiv:2504.01043 · doi:10.1136/jme-2024-109905
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.
arXiv commentJournal of Medical Ethics, forthcoming 2024
References in corpus (6)
- Model Cards for Model Reporting
- Ethical Machine Learning in Health Care
- Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
- Limits of trust in medical AI
- The promise and perils of AI in medicine
- Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges