Mike Schaekermann

schoolUniversity of Waterloobar_chart#2349 Global Rank

Total Papers

HCI Rank

#2349

Research Areas

Years Active

Recent Publications

Towards Better Health Conversations: The Benefits of Context-seeking

Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inaccurate, biased or misleading. We present insights from 5 mixed-methods studies (total N=261), examining how people interact with LLMs for t…

CHI '201st 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…

CHI '201st AuthorMedical & Scientific Data Visualization +1

Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment

Medical data labeling workflows critically depend on accurate assessments from human experts. Yet human assessments can vary markedly, even among medical experts. Prior research has demonstrated benefits of labeler training on performance. Here we utilized two types of labeler training feedback: highlighting incorrect…

CSCW '191st 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 '181st 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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