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Monitoring Human Dependence On AI Systems With Reliance Drills

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arxiv 2409.14055 v5 pith:DF34UVYE submitted 2024-09-21 cs.CY

classification cs.CY
keywords drillsreliancehumanstheyadviceai-generatedhumanmistakes
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AI systems are assisting humans with increasingly diverse intellectual tasks but are still prone to mistakes. Humans are over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify such instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. Our paper examines the reasons why an organisation might choose to implement reliance drills and the doubts they may have about doing so. As an example, we consider the benefits and risks that could arise when using these drills to detect over-reliance on AI in healthcare professionals. We conclude by arguing that reliance drills should become a standard risk management practice for ensuring humans remain appropriately involved in the oversight of AI-assisted decisions.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration

    cs.AI 2025-06 conditional novelty 7.0 of 10

    Model benchmark performance only weakly predicts how well people learn from AI explanations, with notable outliers across code and math.

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