57 citations · 68 across the 8 of their papers we have counts for
8 papers
Addressing the Selection Problem in Explainable AI
Claire Vlases, Katelyn Morrison
Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argu…
A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology
Katelyn Morrison, Arpit Mathur, Aidan Bradshaw +7
As text-to-image generative models rapidly improve, AI researchers are making significant advances in developing domain-specific models capable of generating complex medical imager…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support
Venkatesh Sivaraman, Katelyn Morrison, Will Epperson +1
As AI-based clinical decision support (AI-CDS) is introduced in more and more aspects of healthcare services, HCI research plays an increasingly important role in designing for com…
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
Philipp Spitzer, Joshua Holstein, Katelyn Morrison +3
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explaina…
AI-Powered Reminders for Collaborative Tasks: Experiences and Futures
Katelyn Morrison, Shamsi Iqbal, Eric Horvitz
Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook co…
The Impact of Imperfect XAI on Human-AI Decision-Making
Katelyn Morrison, Philipp Spitzer, Violet Turri +3
Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated ho…
Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers
Katelyn Morrison, Benjamin Gilby, Colton Lipchak +2
Recently, vision transformers and MLP-based models have been developed in order to address some of the prevalent weaknesses in convolutional neural networks. Due to the novelty of…