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AI Explainability, Trust, and Calibration / LLM Trust and Over/Under-Reliance

Users cannot trust the transparency of AI systems, leading to reluctance to adopt them widely.

lightbulbPractical problemLLM Trust and Over/Under-Reliance

Users cannot trust the transparency of AI systems, leading to reluctance to adopt them widely.

CUI '23Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

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lightbulbPractical problemLLM Trust and Over/Under-Reliance

When operating multi-sensor smart devices, environments are complex and misoperations occur frequently.

IUI '25A Dynamic Bayesian Network Based Framework for Multimodal Context-Aware Interactions

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Users struggle to understand complex AI ecosystems, affecting privacy decisions.

IUI '25Mental Models of Generative AI Chatbot Ecosystem

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Ordinary users cannot effectively understand the behavioral logic and information sources of complex generative AI.

IUI '25Building Appropriate Mental Models: What Users Know and Want to Know about an Agentic AI Chatbot

lightbulbPractical problemLLM Trust and Over/Under-Reliance

Users' inability to appropriately trust LLMs leads to wrong decisions or missed opportunities.

CHI '25To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models

lightbulbPractical problemLLM Trust and Over/Under-Reliance

Practitioners struggle to manage the large-scale, complex data required for LLM development.

CHI '25Emerging Data Practices: Data Work in the Era of Large Language Models

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Existing text evaluation systems have limited coverage and unreliable results, affecting education and news generation applications.

CHI '25Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation

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