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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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In search of a Goldilocks zone for credible AI.

Kevin Allan1, Nir Oren2, Jacqui Hutchison3

  • 1School of Psychology, University of Aberdeen, Aberdeen, AB24 2UB, UK. k.allan@abdn.ac.uk.

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Summary

Humans should find a balance in trusting artificial intelligence (AI). Social cognition mechanisms, which regulate human conformity, also influence trust in AI, mirroring human-to-human interactions.

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Establishing appropriate trust in artificial intelligence (AI) is crucial for its societal integration.
  • Overestimating or underestimating AI trustworthiness can hinder its problem-solving potential.
  • Social cognition mechanisms, vital for human conformity, may play a role in regulating human-AI interactions.

Purpose of the Study:

  • To investigate whether human-like conformity biases occur in human interactions with AI.
  • To test the hypothesis that social cognition regulates AI's influence by adapting conformity.
  • To explore how credibility, likelihood, and accuracy affect trust and conformity in AI.

Main Methods:

  • A collaborative remembering paradigm was employed.
  • Participants made decisions about scene content presented by either AI or human sources.
  • Source credibility, decision likelihood, and objective accuracy were manipulated across three experiments.

Main Results:

  • AI sources induced conformity biases similar to human sources.
  • Participants showed increased conformity towards more credible AI sources.
  • Sensitivity to AI source accuracy heightened when participants' own cognitive capacity was reduced.

Conclusions:

  • Social cognition mechanisms appear to regulate human trust and conformity in AI.
  • Findings support the role of social cognition in managing AI's influence.
  • This research opens new avenues for understanding and optimizing human-AI interaction.