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Keywords:
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Area of Science:

  • Decision science
  • Human-computer interaction
  • Cognitive psychology

Background:

  • Algorithmic decision-makers offer potential benefits over human judgment.
  • However, user adoption is hindered by factors related to uncertainty and perceived error.

Purpose of the Study:

  • To investigate whether people will adopt algorithmic decision-makers that outperform humans.
  • To understand the role of uncertainty in the preference for algorithmic versus human decision-making.

Main Methods:

  • Nine studies involving 4,820 participants were conducted.
  • Participants evaluated decision-making methods in domains with varying levels of uncertainty.
  • Preference for algorithmic vs. human judgment was assessed based on performance and perceived error.

Main Results:

  • People exhibit diminishing sensitivity to forecasting errors.
  • Reduced likelihood of using optimal algorithms in more unpredictable decision domains.
  • Preference for decision methods perceived to yield near-perfect outcomes and higher performance variance.

Conclusions:

  • User acceptance of advanced algorithms, like self-driving cars and virtual doctors, is contingent on domain uncertainty.
  • People may reject superior algorithms in uncertain fields such as investing and medicine.
  • Understanding diminishing sensitivity to error is crucial for designing and implementing AI decision-support systems.