Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment

Key points

• Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available.1
• However, little is known about how novice users calibrate reliance on AI when external feedback is unavailable, or whether AI explanations can support calibration in its absence.1
• Through a between-subjects study with 110 participants completing a clinical entity extraction task with AI assistance and limited performance feedback, we observe that novice users exhibit systematic drift toward over-reliance in the presence of explanations, while higher self-reported task understanding is associated with more selective reliance behavior.1
• These results extend reliance calibration research into human-AI collaboration contexts without real-time performance signals and present actionable guidelines on designing AI tools that must support appropriate reliance in these settings.1

Sources (1)

• [1]Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 05:48 AM

  We introduce reliance calibration as an organizing construct for studying how novice users dynamically adjust reliance behavior, and examine how AI explanations and meta-cognitive self-assessment shape it.

  Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available.

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