Jordan Boyd-Graber

schoolUniversity Of Marylandbar_chart#4987 Global Rank

Total Papers

HCI Rank

#4987

Research Areas

Years Active

Recent Publications

No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML

Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users' perceptions of ML models with or without…

IUI '19CorrespondingHuman-LLM Collaboration +2

What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human…

IUI '183rd AuthorHuman-LLM Collaboration +1

Closing the Loop: User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System

Human-in-the-loop topic modeling allows end users to guide the creation of topic models and improve the models' quality without requiring expertise in topic modeling algorithms. Prior work in this area either focuses on refinement implementation without understanding how users actually wish to improve the model or foc…

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