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A Quantum Probability Approach to Improving Human-AI Decision Making.

Entropy (Basel, Switzerland)·2025
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Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making.

Scott Humr1, Mustafa Canan1

  • 1Department of Information Sciences, Naval Postgraduate School, Monterey, CA 93943, USA.

Entropy (Basel, Switzerland)
|June 26, 2024
PubMed
Summary

Intermediate judgments on artificial intelligence (AI) advice bolster trust but can lead to probability violations in human decision-making. Understanding these human-AI interaction dynamics is crucial for effective AI integration.

Keywords:
artificial intelligencedecision-makingquantum decision theoryquantum open systems modelingtrust

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence Ethics

Background:

  • Artificial intelligence (AI) systems are increasingly integrated into human decision-making processes across various fields.
  • Human rationalization of AI outputs is essential for beneficial outcomes.
  • Prior research indicates intermediate judgments can bias subsequent decisions.

Purpose of the Study:

  • To investigate the influence of intermediate judgments on AI-provided advice in human decision-making.
  • To examine how these judgments affect trust and decision consistency.
  • To explore the application of quantum probability theory in modeling human-AI interaction dynamics.

Main Methods:

  • An online experiment was conducted with 192 participants.
  • Participants made decisions involving AI-provided advice.
  • Intermediate judgments were systematically varied across different timing intervals.

Main Results:

  • A consistent bolstering effect in trust was observed for participants who made intermediate judgments.
  • Violations of total probability occurred across all timing intervals.
  • Quantum probability theory effectively modeled observed human-AI decision behaviors.

Conclusions:

  • Intermediate judgments on AI advice can enhance trust but may introduce cognitive biases.
  • Understanding these biases is key for optimizing human-AI collaboration.
  • Quantum probability offers a framework for analyzing complex human-AI interaction dynamics.