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Area of Science:

  • Human-computer interaction
  • Artificial intelligence
  • Cognitive science

Background:

  • Collaborative decision-making between humans and AI is prevalent in many applications.
  • Effective human reliance on AI agents is crucial for successful human-AI teaming.
  • Evaluating AI agent performance in collaborative tasks requires understanding user interaction.

Purpose of the Study:

  • To investigate the impact of artificial intelligence (AI) recommendation skill and display format on user performance in a strategy game.
  • To determine how users' reliance on AI agents changes based on the AI's skill level.
  • To evaluate an experimental method for assessing human-AI collaboration platforms.

Main Methods:

  • A 2x3 between-subjects factorial experiment was conducted using a strategy game (Connect Four).
  • Independent variables included AI recommendation format (categorical vs. probabilistic) and AI agent training level (low, medium, high).
  • Participants proposed moves, received AI recommendations, and then made their final move over 10 games.

Main Results:

  • User performance improved with highly skilled AI agents but led to uncritical reliance and performance plateaus.
  • Users exhibited under-reliance on lower-skilled AI agents.
  • The format of AI recommendations (categorical or probabilistic) did not significantly affect user skill or choices.

Conclusions:

  • The effectiveness of AI agents in collaborative tasks is contingent upon both the AI's skill level and the user's capacity to learn from its advice.
  • Understanding the behavioral dynamics of human-AI teams is essential for organizations implementing AI decision support systems.
  • The study provides a method for evaluating different AI designs and assessing their performance in human-AI collaborations.