Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). - Been Kim; Martin Wattenberg; Justin Gilmer; Carrie J. Cai; James Wexler; Fernanda B. Viégas; Rory Sayres (ICML, 2018)

Scalable and accurate deep learning for electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Peter J. Liu; Xiaobing Liu; Mimi Sun; Patrik Sundberg; Hector Yee; Kun Zhang 043; Gavin E. Duggan; et al. (CoRR, 2018)

ClinicalVis: Supporting Clinical Task-Focused Design Evaluation. - Marzyeh Ghassemi; Mahima Pushkarna; James Wexler; Jesse Johnson; Paul Varghese (CoRR, 2018)

Scalable and accurate deep learning with electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Michaela Hardt; Peter J. Liu; Xiaobing Liu; Jake Marcus; Mimi Sun; Patrik Sundberg; Hector Yee; et al. (npj Digit. Medicine, 2018)

Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. - Kanit Wongsuphasawat; Daniel Smilkov; James Wexler; Jimbo Wilson; Dandelion Mané; Doug Fritz; Dilip Krishnan; Fernanda B. Viégas; Martin Wattenberg (IEEE Trans. Vis. Comput. Graph., 2018)

Approachable Music Composition with Machine Learning at Scale. - Cheng-Zhi Anna Huang; Curtis Hawthorne; Adam Roberts; Monica Dinculescu; James Wexler; Leon Hong; Jacob Howcroft (ISMIR, 2019)

Towards Automatic Concept-based Explanations. - Amirata Ghorbani; James Wexler; James Y. Zou; Been Kim (NeurIPS, 2019)

Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. - Amirata Ghorbani; James Wexler; Been Kim (CoRR, 2019)

The Bach Doodle: Approachable music composition with machine learning at scale. - Cheng-Zhi Anna Huang; Curtis Hawthorne; Adam Roberts; Monica Dinculescu; James Wexler; Leon Hong; Jacob Howcroft (CoRR, 2019)

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. - Ian Tenney; James Wexler; Jasmijn Bastings; Tolga Bolukbasi; Andy Coenen; Sebastian Gehrmann; Ellen Jiang; Mahima Pushkarna; Carey Radebaugh; Emily Reif; Ann Yuan (EMNLP (Demos), 2020)

Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial. - James Wexler; Mahima Pushkarna; Sara Robinson; Tolga Bolukbasi; Andrew Zaldivar (FAT*, 2020)

The What-If Tool: Interactive Probing of Machine Learning Models. - James Wexler; Mahima Pushkarna; Tolga Bolukbasi; Martin Wattenberg; Fernanda B. Viégas; Jimbo Wilson (IEEE Trans. Vis. Comput. Graph., 2020)

Best of both worlds: local and global explanations with human-understandable concepts. - Jessica Schrouff; Sebastien Baur; Shaobo Hou; Diana Mincu; Eric Loreaux; Ralph Blanes; James Wexler; Alan Karthikesalingam; Been Kim (CoRR, 2021)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey. - Narges Ashtari; Ryan Mullins; Crystal Qian; James Wexler; Ian Tenney; Mahima Pushkarna (Conference on Designing Interactive Systems, 2023)

ConstitutionalExperts: Training a Mixture of Principle-based Prompts. - Savvas Petridis; Ben Wedin; Ann Yuan; James Wexler; Nithum Thain (ACL (Short Papers), 2024)

LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models. - Minsuk Kahng; Ian Tenney; Mahima Pushkarna; Michael Xieyang Liu; James Wexler; Emily Reif; Krystal Kallarackal; Minsuk Chang; Michael Terry; Lucas Dixon (CHI Extended Abstracts, 2024)

Automatic Histograms: Leveraging Language Models for Text Dataset Exploration. - Emily Reif; Crystal Qian; James Wexler; Minsuk Kahng (CHI Extended Abstracts, 2024)

ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles. - Savvas Petridis; Benjamin D. Wedin; James Wexler; Mahima Pushkarna; Aaron Donsbach; Nitesh Goyal; Carrie J. Cai; Michael Terry (IUI, 2024)

Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration. - Crystal Qian; James Wexler (IUI, 2024)

The Evolution of LLM Adoption in Industry Data Curation Practices. - Crystal Qian; Michael Xieyang Liu; Emily Reif; Grady Simon; Nada Hussein; Nathan Clement; James Wexler; Carrie J. Cai; Michael Terry; Minsuk Kahng (CoRR, 2024)

LLM Adoption in Data Curation Workflows: Industry Practices and Insights. - Crystal Qian; Michael Xieyang Liu; Emily Reif; Grady Simon; Nada Hussein; Nathan Clement; James Wexler; Carrie J. Cai; Michael Terry; Minsuk Kahng (CHI Extended Abstracts, 2025)

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models. - Minsuk Kahng; Ian Tenney; Mahima Pushkarna; Michael Xieyang Liu; James Wexler; Emily Reif; Krystal Kallarackal; Minsuk Chang; Michael Terry; Lucas Dixon (IEEE Trans. Vis. Comput. Graph., 2025)