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People prefer artificial intelligence (AI) when it seems more capable and personalization isn't needed. Otherwise, AI aversion occurs, showing context matters for AI appreciation and AI aversion.

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Area of Science:

  • Psychology
  • Human-Computer Interaction
  • Artificial Intelligence Studies

Background:

  • Conflicting findings exist regarding human preference for artificial intelligence (AI) over humans, with some studies showing AI appreciation and others AI aversion.
  • Existing research lacks a unifying framework to explain these divergent outcomes in human-AI interaction.

Purpose of the Study:

  • To introduce and validate the Capability-Personalization Framework to reconcile conflicting findings on AI appreciation and AI aversion.
  • To theoretically explain when individuals prefer AI over humans based on perceived AI capability and the necessity for personalization.

Main Methods:

  • A meta-analysis was conducted, synthesizing 442 effect sizes from 163 studies with a total of 82,078 participants.
  • The framework was tested by examining the conditions under which AI appreciation (preference for AI) versus AI aversion (preference for humans) occurs.

Main Results:

  • AI appreciation was observed when AI was perceived as more capable than humans and personalization was deemed unnecessary (d = 0.27).
  • AI aversion was prevalent when these conditions were not met (d = -0.50).
  • Moderators included AI form (robots vs. algorithms), outcome type (attitudinal vs. behavioral), study design, and country-level economic and educational factors.

Conclusions:

  • The Capability-Personalization Framework effectively explains human preferences in AI-human decision-making contexts.
  • Findings offer critical insights for AI developers and users aiming to optimize human-AI collaboration and acceptance.
  • Understanding the interplay of capability and personalization is key to navigating the evolving landscape of artificial intelligence.