Mina Farid, PhD candidate
David R. Cheriton School of Computer Science
Mina Farid, PhD candidate
David R. Cheriton School of Computer Science
One challenge that faces most extraction tools is the long tail of information. Entities that lie in the long tail do not have enough mentions in the text, limiting their relevant context. The absence of enough repetition restricts the extraction of property values with high confidence.
Sasha Vtyurina, PhD candidate
David R. Cheriton School of Computer Science
Voice-based assistants have become a popular tool for conducting web search, particularly for factoid question answering. However, for more complex web searches their functionality remains limited, as does our understanding of the ways in which searchers can best interact with audio-based search results.
Nashid Shahriar, PhD candidate
David R. Cheriton School of Computer Science
Anil Pacaci, PhD candidate
David R. Cheriton School of Computer Science
Mike Schaekermann, PhD candidate
David R. Cheriton School of Computer Science
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.
Christian Gorenflo, PhD candidate
David R. Cheriton School of Computer Science
Jan Gorzny, PhD candidate
David R. Cheriton School of Computer Science
Ryan Clancy, Master’s candidate
Kate Larson
David R. Cheriton School of Computer Science
Axiomatic approaches are an appealing method for designing fair algorithms, as they provide a formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms.
We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory for the fair division of rewards.