arXiv:2509.08010v2 Announce Type: replace-cross Abstract: Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural language on a range of tasks. As LLMs increasingly influence consequential decisions across diverse domains from healthcare to personal advice, the risk of overreliance -- relying on LLMs beyond their capabilities -- grows. This paper argues that measuring and mitigating overreliance must become central to LLM research and deployment. First, we consolidate risks
The rapid proliferation and increasing sophistication of large language models are making concerns about human overreliance critically relevant now, as these models are deployed in sensitive domains.
This paper highlights a critical and emerging risk in AI adoption, suggesting that unchecked overreliance could lead to significant real-world failures and erode trust in AI systems.
The focus on measuring and mitigating overreliance shifts the AI development paradigm from pure capability enhancement to responsible deployment and human-AI interaction design.
- · AI ethics researchers
- · AI safety organizations
- · Human-AI interface designers
- · Regulations adhering AI developers
- · Developers prioritizing speed over safety
- · Organizations deploying unchecked AI systems
- · Users unaware of AI limitations
- · AI systems lacking transparency
Increased research and development into explainable AI, human-in-the-loop systems, and AI literacy programs.
Regulatory bodies may mandate assessments of overreliance risk for AI systems used in critical applications like healthcare or finance.
Public distrust in AI could grow if high-profile incidents of overreliance lead to significant negative outcomes, potentially slowing AI adoption.
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Read at arXiv cs.AI