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Explainability in context: calibrating appropriate trust and reliance in artificial intelligence
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DOI：10.1093/jamia/ocag082.png)
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Abstract
En 中文
Predictive artificial intelligence (AI) promises to transform care delivery, enhance patient safety, and improve health outcomes. Realizing these benefits will require careful design, implementation, and monitoring strategies to avoid unintended consequences, including automation bias (i.e., erroneously favoring recommendations from automated systems). Automation bias is particularly concerning due to the variability of AI performance across time and populations, leading to predictions that may be variably incorrect, uncertain, or unfair.
Keywords:
Artificial Intelligence
Predictive Analytics
Automation Bias
Trust in AI
Explainability
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