Having appropriate reliance on AI is key to harnessing the benefits of AI technology and achieving human-AI complementarity; while inappropriate reliance, particularly overreliance on AI, can lead to a range of harms, from high-stakes errors, de-skilling, to infrastructural vulnerabilities. Since before this wave of LLM technology, the field of human-computer interaction (HCI) has been studying how to facilitate appropriate reliance on AI, through empirical investigation of how people choose to rely or not rely on AI, designing interventions to mitigate inappropriate reliance, and developing approaches to measure and model people’s reliance behaviors. In this talk, I will provide an overview of these lines of HCI research and pose three open questions in the age of LLMs: How should we grapple with the normative question of what constitutes appropriate reliance? How can we measure and monitor reliance without intensive behavior surveillance? How can we deliver targeted preventive interventions to prevent overreliance by accounting for the system, individual, and contextual risk factors?
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