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Updated: Feb 28, 2026

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Human-AI collaboration: trade-offs between performance and preferences.

Lukas W Mayer1, Sheer Karny2, Jackie Ayoub3

  • 1Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA. lwmayer@uci.edu.

Cognitive Research: Principles and Implications
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

People prefer collaborative artificial intelligence (AI) agents that consider human actions over those focused solely on performance. Human-centric AI design enhances likability without sacrificing effectiveness, promoting meaningful human contributions.

Keywords:
Bayesian modeling of preferencesDynamic decision-makingHuman-centered algorithm designHuman–AI collaboration

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Integrating human input into collaborative artificial intelligence (AI) systems presents significant design challenges.
  • Understanding human preferences is crucial for developing effective human-AI collaboration.

Purpose of the Study:

  • To systematically investigate human preferences for collaborative AI agents with varying adaptation strategies.
  • To identify factors influencing human choices in human-AI collaboration.

Main Methods:

  • Developed a task to evaluate five collaborative AI agents with distinct adaptation strategies.
  • Participants interacted with agents, assessed their traits, and selected preferred agents.
  • Employed a Bayesian model to analyze agent strategies, team performance, perceived AI traits, and human preferences.

Main Results:

  • Collaborative AI agents considerate of human actions were preferred over performance-maximizing agents.
  • Human-centric AI design improved AI collaborator likability without compromising team performance.
  • Inequality aversion was identified as a key driver, with humans preferring agents enabling meaningful contributions.

Conclusions:

  • Human-centric design principles are vital for developing likable and effective AI collaborators.
  • Balancing subjective preferences with objective performance metrics is essential for successful human-AI collaboration.
  • Addressing human desires for meaningful contribution can enhance the acceptance and utility of AI systems.