138 citations · 251 across the 11 of their papers we have counts for
13 papers
Can LLM Code Explanations Adapt to Diverse Problem-Solvers' Needs?
Andrew Anderson, David Piorkowski, Justin Weisz +2
Large language model (LLM) code explanations can support people in solving code-related problems, yet prior work has shown that people have diverse problem-solving styles. If expla…
An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?
Andrew Anderson, David Piorkowski, Margaret Burnett +1
Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. W…
Supporting Annotators with Affordances for Efficiently Labeling Conversational Data
Austin Z. Henley, David Piorkowski
Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labe…
Quantitative AI Risk Assessments: Opportunities and Challenges
David Piorkowski, Michael Hind, John Richards
Although AI systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified and have manifest…
Evaluating a Methodology for Increasing AI Transparency: A Case Study
David Piorkowski, John Richards, Michael Hind
In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are crea…
Iterative Design of Gestures During Elicitation: Understanding the Role of Increased Production
Andreea Danielescu, David Piorkowski
Previous gesture elicitation studies have found that user proposals are influenced by legacy bias which may inhibit users from proposing gestures that are most appropriate for an i…