Monitoring Human Dependence On AI Systems With Reliance Drills

• URL: http://arxiv.org/abs/2409.14055v2
• Date: Thu, 26 Sep 2024 10:54:20 GMT
• Title: Monitoring Human Dependence On AI Systems With Reliance Drills
• Authors: Rosco Hunter, Richard Moulange, Jamie Bernardi, Merlin Stein,
• Abstract summary: Humans could be over-reliant on AI systems if they trust AI-generated advice, even though they would make a better decision on their own. This paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. We argue that reliance drills could become a key tool for ensuring humans remain appropriately involved in AI-assisted decisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI systems are assisting humans with an increasingly broad range of intellectual tasks. Humans could be over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify real-world instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. We introduce a pipeline that organisations could use to implement these drills. As an example, we explain how this approach could be used to limit over-reliance on AI in a medical setting. We conclude by arguing that reliance drills could become a key tool for ensuring humans remain appropriately involved in AI-assisted decisions.

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