5 papers
CHiQPM: Calibrated Hierarchical Interpretable Image Classification
Thomas Norrenbrock, Timo Kaiser, Sovan Biswas +3
Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial compl…
Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines
Ramesh Manuvinakurike, Emanuel Moss, Elizabeth Anne Watkins +3
Agentic pipelines present novel challenges and opportunities for human-centered explainability. The HCXAI community is still grappling with how best to make the inner workings of L…
ACE, Action and Control via Explanations: A Proposal for LLMs to Provide Human-Centered Explainability for Multimodal AI Assistants
Elizabeth Anne Watkins, Emanuel Moss, Ramesh Manuvinakurike +3
In this short paper we address issues related to building multimodal AI systems for human performance support in manufacturing domains. We make two contributions: we first identify…
QPM: Discrete Optimization for Globally Interpretable Image Classification
Thomas Norrenbrock, Timo Kaiser, Sovan Biswas +2
Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a si…
QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing
Ramesh Manuvinakurike, Elizabeth Watkins, Celal Savur +7
In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system. The dataset consists of representative samples of interactions with technicians…