16 citations · 16 across the 2 of their papers we have counts for
2 papers
cs.HC2026
Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs)
Marvin Pafla, Jesse Hoey, Kate Larson +1
Explainability is often framed as a property of an AI model, with explanations extracted from its internals and shown to users. In this argument paper, we instead provide an embodi…
cs.HC2024★ 16 cited
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models
Marvin Pafla, Kate Larson, Mark Hancock
The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e.g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has ex…