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Area of Science:

  • Cognitive Psychology
  • Artificial Intelligence Ethics
  • Clinical Decision Support

Background:

  • Algorithm aversion is the tendency to distrust algorithmic advice, even when superior to human input.
  • Understanding the cognitive basis of algorithm aversion is crucial for effective human-AI collaboration in critical fields like medicine.

Purpose of the Study:

  • To investigate the cognitive mechanisms driving algorithm aversion in a clinical decision-making context.
  • To differentiate between increased caution and reduced perceived reliability as causes of algorithm aversion.

Main Methods:

  • Two experiments involving participants evaluating X-rays for bone fractures with advice from either an algorithm or a human.
  • Utilized evidence accumulation modeling to analyze decision thresholds and response times.
  • Compared decision strategies when human advice was attributed to laypersons versus expert radiologists.

Main Results:

  • Participants exhibited longer response times and set higher decision thresholds for algorithmic advice, indicating increased deliberation and caution.
  • This heightened caution persisted regardless of whether human advice came from lay participants or expert radiologists.
  • No significant differences were found in evidence accumulation rates or prior preferences between algorithmic and human advice sources.

Conclusions:

  • Algorithm aversion is characterized by a strategic increase in decision caution rather than a diminished perception of algorithmic reliability.
  • Formal cognitive models are valuable for understanding trust in automated systems and optimizing human-algorithm collaboration.
  • Identifying response caution as a core mechanism advances the theoretical understanding of algorithm aversion.