The false hope of current approaches to explainable artificial intelligence in health care
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- Teacher spread
- 0.045 · how far apart the two teachers sit on this one work
- Validation status
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Abstract
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. 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. 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. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients. In the absence of suitable explainability methods, we advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability, and we caution against having explainability be a requirement for clinically deployed models.
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The record
- Venue
- The Lancet Digital Health
- Topic
- Explainable Artificial Intelligence (XAI)
- Field
- Computer Science
- Canadian institutions
- Vector Institute
- Funders
- National Heart, Lung, and Blood Institute
- Keywords
- Transparency (behavior)Argument (complex analysis)Process (computing)Black boxComputer scienceWorkforceHealth careArtificial intelligencePsychologyRisk analysis (engineering)MedicinePolitical scienceComputer securityLaw
- Has abstract in OpenAlex
- yes