activity
20202026
most citedHuman Factors in Model Interpretability: Industry Practices, Challenges, and Needs

215 citations · 271 across the 10 of their papers we have counts for

collaborators

10 papers

cs.HC2026

Designing Multi-Robot Ground Video Sensemaking with Public Safety Professionals

Puqi Zhou, Ali Asgarov, Aafiya Hussain +9

Videos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals' burden. Yet little is known about how to desig…

cs.HC2024

Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic Systems

Zinat Ara, Hossein Salemi, Sungsoo Ray Hong +4

Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in do…

cs.HC2024

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Zinat Ara, Amrita Ganguly, Donna Peppard +4

Successful job search results from job seekers' well-shaped social communication. While well-known differences in communication exist between people with autism and neurotypicals,…

cs.HC2024★ 13 cited

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration

Nahyun Kwon, Tong Sun, Yuyang Gao +4

The widespread consumer-grade 3D printers and learning resources online enable novices to self-train in remote settings. While troubleshooting plays an essential part of 3D printin…

cs.HC2023★ 2 cited

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

Tong Steven Sun, Yuyang Gao, Shubham Khaladkar +4

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has bee…

cs.AI2022★ 9 cited

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

Yuyang Gao, Siyi Gu, Junji Jiang +3

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metr…

cs.CV2022★ 29 cited

RES: A Robust Framework for Guiding Visual Explanation

Yuyang Gao, Tong Steven Sun, Guangji Bai +3

Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research que…

cs.AI2022★ 2 cited

Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment

Yuyang Gao, Tong Sun, Liang Zhao +1

While Deep Neural Networks (DNNs) are deriving the major innovations in nearly every field through their powerful automation, we are also witnessing the peril behind automation as…

cs.HC2020★ 1 cited

Towards Evaluating Exploratory Model Building Process with AutoML Systems

Sungsoo Ray Hong, Sonia Castelo, Vito D'Orazio +6

The use of Automated Machine Learning (AutoML) systems are highly open-ended and exploratory. While rigorously evaluating how end-users interact with AutoML is crucial, establishin…

cs.HC2020★ 215 cited

Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs

Sungsoo Ray Hong, Jessica Hullman, Enrico Bertini

As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-perf…