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What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?
Abstract
The prospect of including artificial intelligence 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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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.
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