Extrinsic Trust as a Contractual Framework for Accountable AI in Health Care: Viewpoint

TL;DR

The paper introduces a contractual framework for trustworthy AI in healthcare, focusing on building extrinsic trust through reliability, equity, and managing uncertainty.

Contribution

The novel contribution is a contractual framework for accountable AI in healthcare, emphasizing extrinsic trust through three operational promises.

Findings

A contractual framework is proposed to bridge the trust gap in healthcare AI.

Three promises—reliability, scope and equity, and shift and uncertainty—are defined for operationalizing trustworthy AI.

The framework is illustrated with a healthcare vignette to show practical implementation.

Abstract

Artificial intelligence (AI) promises efficiency and equity in health care. However, adoption remains fragmented due to weak foundations of trust. This Viewpoint highlights the gap between intrinsic trust, based on interpretability, and extrinsic trust, based on functional validation. We propose a contractual framework between AI systems and users defined by 3 promises: reliability, scope and equity, and shift and uncertainty. Illustrated through a vignette, we show how health systems can operationalize these promises through structured evidence and governance, translating trustworthy AI into accountable clinical deployment.

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