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Public attitudes on performance for algorithmic and human decision-makers.

Kirk Bansak1, Elisabeth Paulson2

  • 1Department of Political Science, University of California, 210 Social Sciences Building, Berkeley, CA 94720, USA.

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|December 11, 2024
PubMed
Summary
This summary is machine-generated.

Publics prioritize efficiency over fairness when evaluating human and algorithmic decision-makers (DMs). Most respondents preferred human DMs, even when performance metrics were controlled, indicating nuanced views on AI in high-stakes decisions.

Keywords:
algorithmic decision-makingconjoint experimentspublic opinion

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Area of Science:

  • Social Psychology
  • Decision Science
  • Human-Computer Interaction

Background:

  • Algorithmic decision-makers (DMs) are increasingly used in high-stakes fields.
  • Public trust and acceptance of these systems depend on understanding preferences for human versus algorithmic DMs.
  • Evaluating DM performance requires considering multiple metrics, including efficiency and fairness.

Purpose of the Study:

  • To investigate public preferences for human versus algorithmic decision-makers in high-stakes scenarios.
  • To determine how performance metrics shape these preferences.
  • To examine whether public evaluations of performance differ based on DM type.

Main Methods:

  • A conjoint experimental design was employed with approximately [number] respondents.
  • Respondents evaluated pairs of DM profiles in pretrial release and bank loan approval scenarios.
  • Profiles varied by DM type (human/algorithm) and performance metrics (crime/default rate, false positive rates for white and minority groups, and fairness metric).

Main Results:

  • Efficiency was the most prioritized performance metric, while fairness was the least.
  • This prioritization held across scenarios, respondent subgroups, and DM types.
  • An average preference for human DMs over algorithmic DMs was observed, even after controlling for performance.

Conclusions:

  • Public preferences for decision-maker type are diverse, but there is consistency in desired performance metrics.
  • Efficiency significantly outweighs fairness in public evaluations of DMs.
  • Despite a general preference for human decision-makers, individual variations highlight the complexity of AI adoption in critical decision-making processes.