The false hope of current approaches to explainable artificial intelligence in health care

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias.
• 2 In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support.
• 3 We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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