Daniel S. Weld

schoolUniversity Of Washingtonbar_chart#2991 Global Rank

Total Papers

HCI Rank

#2991

Research Areas

Years Active

Recent Publications

From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augme…

CSCW '212nd AuthorCrowds and Collaboration

Goldilocks: Consistent Crowdsourced Scalar Annotations with Relative Uncertainty

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inhe…

CHI '21CorrespondingExplainable AI (XAI) +1

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanation…

CHI '205th+ AuthorExplainable AI (XAI) +2

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…

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