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The What-If Tool: Interactive Probing of Machine Learning Models., , , , , and . IEEE Trans. Vis. Comput. Graph., 26 (1): 56-65 (2020)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles., , , , , , , and . CoRR, (2023)From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey., , , , , and . Conference on Designing Interactive Systems, page 2304-2325. ACM, (2023)Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI., , and . FAccT, page 1776-1826. ACM, (2022)The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models., , , , , , , , , and 1 other author(s). EMNLP (Demos), page 107-118. Association for Computational Linguistics, (2020)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles., , , , , , , and . IUI, page 853-868. ACM, (2024)GEMv2: Multilingual NLG Benchmarking in a Single Line of Code., , , , , , , , , and 66 other author(s). CoRR, (2022)Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook., , , , and . CHI, page 356:1-356:13. ACM, (2023)Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial., , , , and . FAT*, page 705. ACM, (2020)Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models., , , , , , and . CHI Extended Abstracts, page 54:1-54:15. ACM, (2024)