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Algorithmic advice increased response changes and confidence in creative tasks, but decreased accuracy. Humans showed both appreciation and aversion to AI recommendations, impacting problem-solving outcomes.

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Artificial Intelligence

Background:

  • Algorithms increasingly support creative tasks like text generation.
  • Human responses to algorithmic advice vary, from complacency to mistrust.
  • Understanding human-AI interaction in creative domains is crucial.

Purpose of the Study:

  • To investigate human reactions to algorithmic advice in a creative task.
  • To compare responses to algorithmic versus crowd-sourced advice.
  • To assess the impact of advice quality and question difficulty on human decision-making.

Main Methods:

  • A preregistered online experiment with 154 participants and 2772 observations.
  • Participants completed the Remote Associates Test (RAT) twice, receiving advice in between.
  • Advice varied in source (algorithmic vs. crowd) and quality, and questions varied in difficulty.

Main Results:

  • Individuals receiving algorithmic advice changed responses 13% more frequently and reported higher confidence.
  • However, algorithmic advice led to a 13% decrease in identifying the correct solution.
  • Both algorithmic and crowd advice influenced human responses, with varying effects on accuracy.

Conclusions:

  • Algorithmic advice can enhance confidence and engagement in creative tasks but may reduce accuracy.
  • Human-AI collaboration in creative fields presents both opportunities and challenges.
  • Further research is needed to optimize algorithmic support for creative problem-solving.