The paper consolidates risks of overreliance on LLMs, identifies gaps in current measurement approaches, and proposes mitigation strategies to keep AI as a human-compatible thought partner.
Trust in automation: Designing for appropriate reliance
2 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal explainability module using vision-language models and heat maps enables robots to generate natural-language summaries of navigation observations, with n=30 user studies showing majority preference for real-time explanations and improved trust.
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Measuring and mitigating overreliance to build human-compatible AI
The paper consolidates risks of overreliance on LLMs, identifies gaps in current measurement approaches, and proposes mitigation strategies to keep AI as a human-compatible thought partner.
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Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Models
Multimodal explainability module using vision-language models and heat maps enables robots to generate natural-language summaries of navigation observations, with n=30 user studies showing majority preference for real-time explanations and improved trust.