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Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence.

Georg Starke¹, Marcello Ienca¹

¹College of Humanities, EPFL, 1015Lausanne, Switzerland.

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Summary

This summary is machine-generated.

This study examines misplaced trust in artificial intelligence (AI) within clinical settings. By analyzing negative examples, it offers ethical guidelines for engaging with medical AI systems, avoiding pitfalls in trust.

Keywords:

AI medicine decision-making distrust ethics trust

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Area of Science:

Medical Ethics
Artificial Intelligence in Healthcare
Philosophy of Technology

Background:

Artificial intelligence (AI) is increasingly used in clinical settings, often featuring 'epistemic opacity' that hinders human understanding.
The concept of trust in clinical AI is debated, with concerns about category errors and 'ethics washing'.
Existing literature lacks consensus on defining trust in AI, despite its growing importance.

Purpose of the Study:

To explore the complexities of trust and distrust in medical AI systems.
To develop a taxonomy of misplaced trust and distrust in clinical AI.
To provide ethical constraints for clinical and regulatory decisions regarding AI engagement.

Main Methods:

Adopting an 'ex negativo' approach, focusing on instances of misplaced trust or distrust.
Comparing AI trust/distrust with trust dynamics in doctor-patient relationships.
Systematizing negative examples to form a taxonomy.

Main Results:

Identification of various forms of misplaced trust and distrust in clinical AI.
Development of a novel taxonomy categorizing these instances.
Ethical insights derived from analyzing negative cases of AI engagement.

Conclusions:

Focusing on misplaced trust offers a practical strategy for ethical AI engagement.
The proposed taxonomy provides a framework for understanding and avoiding errors in trusting clinical AI.
This approach yields actionable ethical constraints for clinicians and regulators navigating medical AI.