497 citations · 1.9k across the 38 of their papers we have counts for
6 papers · 1 filter
Seamful XAI: Operationalizing Seamful Design in Explainable AI
Upol Ehsan, Q. Vera Liao, Samir Passi +2
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make the user experience seamless, hiding…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Q. Vera Liao, Yunfeng Zhang, Ronny Luss +2
Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to ev…
Designing for Responsible Trust in AI Systems: A Communication Perspective
Q. Vera Liao, S. Shyam Sundar
Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust…
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Vivian Lai, Samuel Carton, Rajat Bhatnagar +3
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such im…
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun, Q. Vera Liao, Michael Muller +4
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative mo…