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Comparing discriminatory behavior against AI and humans.

Mike Zhuang1, Eliane Deschrijver2, Richard Ramsey3,4

  • 1School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3052, Australia. mikez@unimelb.edu.au.

Scientific Reports
|March 29, 2025
PubMed
Summary
This summary is machine-generated.

Humans show discrimination even with arbitrary choices, surprisingly allocating resources similarly to both human and artificial intelligence (AI) agents. This suggests discriminatory behavior stems more from choice alignment than recipient identity.

Keywords:
Algorithm aversionArtificial intelligenceBiasComputers are social actorsDiscriminationHuman–AI interaction

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Area of Science:

  • Social Psychology
  • Human-Computer Interaction
  • Behavioral Economics

Background:

  • Discrimination typically arises from established categories (e.g., ethnicity, sex).
  • However, discrimination persists even in minimal conditions based on arbitrary shared characteristics.
  • Previous research indicates preferential resource allocation based on factors like dot estimation choices.

Purpose of the Study:

  • To investigate human discriminatory behavior towards artificial intelligence (AI) agents.
  • To compare discrimination against AI versus human agents in a resource allocation task.
  • To test the hypothesis that algorithm aversion increases discrimination against AI.

Main Methods:

  • A preregistered experiment involving 500 participants was conducted.
  • Participants interacted with AI agents and human agents who ostensibly made dot estimations.
  • Resource allocation patterns were analyzed to identify discriminatory behavior.

Main Results:

  • Participants distributed resources unequally to both human and AI agents.
  • No significant difference was found in the strength of discrimination between human and AI agents.
  • Resource allocation was primarily influenced by decision alignment (choice congruency).

Conclusions:

  • Human discriminatory behavior is not solely dependent on the recipient's identity (human vs. AI).
  • Choice congruency, or alignment of decisions, plays a more significant role in shaping discrimination.
  • The findings challenge the notion that algorithm aversion universally amplifies discrimination against AI.