Search Results for author: Marissa Radensky

Found 4 papers, 0 papers with code

Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare

no code implementations27 Apr 2022 Marissa Radensky, Dustin Burson, Rajya Bhaiya, Daniel S. Weld

An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions.

Decision Making

Exploring The Role of Local and Global Explanations in Recommender Systems

no code implementations27 Sep 2021 Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popović, Daniel S. Weld

However, we note that the two explanation approaches may be better compared in the context of a higher-stakes or more opaque domain.

Recommendation Systems

Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

no code implementations NeurIPS Workshop AI4Scien 2021 Jason Portenoy, Marissa Radensky, Jevin West, Eric Horvitz, Daniel Weld, Tom Hope

We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars.

Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations

no code implementations30 Aug 2019 Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, Brad A. Myers

In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations.

Human-Computer Interaction

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