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What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?
Canadian Journal of Bioethics / Revue canadienne de bioéthique 4 (1):94-100 (2021)
Abstract
The prospect of including artificial intelligence (AI) in clinical decision-making is an exciting next step for some areas of healthcare. This article provides an analysis of the available kinds of AI systems, focusing on macro-level characteristics. This includes examining the strengths and weaknesses of opaque systems and fully explainable systems. Ultimately, the article argues that “grey box” systems, which include some combination of opacity and transparency, ought to be used in healthcare settings.Author's Profile
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Citations of this work
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Machine learning, healthcare resource allocation, and patient consent.Jamie Webb - 2024 - The New Bioethics 30 (3):206-227.
Naming and Diffusing the Understanding Objection in Healthcare Artificial Intelligence.Jordan Wadden - 2024 - Canadian Journal of Bioethics / Revue canadienne de bioéthique 7 (4):57-63.
References found in this work
Artificial Intelligence and Black‐Box Medical Decisions: Accuracy versus Explainability.Alex John London - 2019 - Hastings Center Report 49 (1):15-21.
Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2019 - Philosophy and Technology 34 (2):265-288.
On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning.Justin B. Biddle - 2022 - Canadian Journal of Philosophy 52 (3):321-341.
Feminist AI: Can We Expect Our AI Systems to Become Feminist?Galit Wellner & Tiran Rothman - 2020 - Philosophy and Technology 33 (2):191-205.
Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?I. Glenn Cohen - 2020 - SSRN Electronic Journal.
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