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Area of Science:

  • Psychology
  • Human-Computer Interaction
  • Artificial Intelligence Ethics

Background:

  • Human moral interactions typically involve equitable resource allocation.
  • The application of these moral assumptions to artificial intelligence (AI) entities is not well understood.
  • Investigating human responses to AI fairness is crucial for developing ethical AI.

Purpose of the Study:

  • To examine how humans respond to fair, disadvantageous, and advantageous offers from AI compared to human counterparts.
  • To analyze behavioral, physiological, and affective responses in human-AI interactions.
  • To propose a model for understanding human responses to AI moral behavior.

Main Methods:

  • Utilized a 21-round Ultimatum Game to simulate resource allocation scenarios.
  • Compared participant responses to offers from an AI versus a human counterpart.
  • Measured behavioral (rejection rates), physiological (heart rate variability), and affective (emotional responses) data.

Main Results:

  • Participants rejected disadvantageous offers from AI more frequently than from humans.
  • Participants rejected advantageous offers from humans more frequently than from AI.
  • Negative affect and physiological stress responses (heart rate variability) were heightened for disadvantageous AI offers.

Conclusions:

  • Human responses to AI fairness differ significantly from responses to human fairness.
  • Self-regulatory processes play a key role in how humans perceive and react to AI moral behavior.
  • Findings inform the development of more ethically aligned AI systems.