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The impact of AI errors in a human-in-the-loop process.

Ujué Agudo1,2, Karlos G Liberal1, Miren Arrese1

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Cognitive Research: Principles and Implications
|January 7, 2024
PubMed
Summary
This summary is machine-generated.

Human judgment accuracy decreases with incorrect artificial intelligence (AI) support in automated decision-making. Receiving AI feedback before judgment, especially when flawed, significantly impairs decision accuracy.

Keywords:
AIArtificial intelligenceAutomation biasComplianceDecision-makingHuman-in-the-loopHuman–computer interaction

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Area of Science:

  • Human-Computer Interaction
  • Public Sector Technology
  • Algorithmic Decision-Making

Background:

  • Automated decision-making is expanding in the public sector.
  • Human oversight is recommended to mitigate algorithmic bias and errors.
  • Existing research on human-in-the-loop systems lacks conclusive findings on benefits, risks, and influencing factors.

Purpose of the Study:

  • To investigate the impact of human-in-the-loop artificial intelligence (AI) support on decision-making accuracy.
  • To examine how the timing of AI feedback (before or after human judgment) affects outcomes.
  • To identify specific human-computer interaction aspects influencing automated decision processes.

Main Methods:

  • Two experiments simulating automated decision-making in a legal context.
  • Participants judged defendants and crimes, with manipulated timing of AI support.
  • Independent variable: timing of AI feedback (pre-judgment vs. post-judgment).

Main Results:

  • Incorrect AI support negatively impacts human judgment accuracy.
  • Pre-judgment AI feedback leads to greater accuracy reduction compared to post-judgment feedback.
  • The timing of human-computer interaction with AI is a critical factor in decision quality.

Conclusions:

  • Human oversight in automated systems can be detrimental if the AI provides incorrect information.
  • The timing of AI feedback is crucial; receiving flawed AI input before judgment significantly reduces accuracy.
  • Further research is needed to optimize human-AI collaboration in public sector automated decision-making.