Kate Larson

schoolUniversity of Waterloobar_chart#2487 Global Rank

Total Papers

HCI Rank

#2487

Research Areas

Years Active

Recent Publications

Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models

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 exploded with the rise of deep learning (DL). However, human-participant studies question the efficacy of these methods, particularly when th…

CHI '205th+ AuthorExplainable AI (XAI) +1

Ambiguity-aware AI Assistants for Medical Data Analysis

Artificial intelligence (AI) assistants for clinical decision making show increasing promise in medicine. However, medical assessments can be contentious, leading to expert disagreement. This raises the question of how AI assistants should be designed to handle the classification of ambiguous cases. Our study compared…

CSCW '193rd AuthorCrowds and Collaboration

Paying Crowd Workers for Collaborative Work

Collaborative crowdsourcing tasks allow crowd workers to solve problems that they could not handle alone, but worker motivation in these tasks is not well understood. In this paper, we study how to motivate groups of workers by paying them equitably. To this end, we characterize existing collaborative tasks based on t…

CSCW '195th+ AuthorExpert Work

Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication

Expert disagreement is pervasive in clinical decision making and collective adjudication is a useful approach for resolving divergent assessments. Prior work shows that expert disagreement can arise due to diverse factors including expert background, the quality and presentation of data, and guideline clarity. In this…

CSCW '183rd AuthorClassification and Labels

Resolvable vs. Irresolvable Disagreement: A Study on Worker Deliberation in Crowd Work

Crowdsourced classification of data typically assumes that objects can be unambiguously classified into categories. In practice, many classification tasks are ambiguous due to various forms of disagreement. Prior work shows that exchanging verbal justifications can significantly improve answer accuracy over aggregatio…

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