{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:12:19Z","timestamp":1776121939707,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539074","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4132-4142","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values"],"prefix":"10.1145","author":[{"given":"Zijie J.","family":"Wang","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Alex","family":"Kale","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Harsha","family":"Nori","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Peter","family":"Stella","sequence":"additional","affiliation":[{"name":"NYU Langone Health, New York City, NY, USA"}]},{"given":"Mark E.","family":"Nunnally","sequence":"additional","affiliation":[{"name":"NYU Langone Health, New York City, NY, USA"}]},{"given":"Duen Horng","family":"Chau","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Mihaela","family":"Vorvoreanu","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Jennifer","family":"Wortman Vaughan","sequence":"additional","affiliation":[{"name":"Microsoft Research, New York City, NY, USA"}]},{"given":"Rich","family":"Caruana","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2018. Lending Club: Online Personal Loans. https:\/\/www.lendingclub.com\/"},{"key":"e_1_3_2_2_2_1","volume-title":"Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS","author":"Agarwal Rishabh","year":"2021","unstructured":"Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E Hinton. 2021. Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS (2021)."},{"key":"e_1_3_2_2_3_1","volume-title":"Statistical Inference under Order Restrictions: The Theory and Application of Isotonic Regression","author":"Barlow Richard E.","unstructured":"Richard E. Barlow. 1972. Statistical Inference under Order Restrictions: The Theory and Application of Isotonic Regression. Wiley, London, New York."},{"key":"e_1_3_2_2_4_1","volume-title":"Identifying and Controlling Important Neurons in Neural Machine Translation. In International Conference on Learning Representations.","author":"Bau Anthony","year":"2019","unstructured":"Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2019. Identifying and Controlling Important Neurons in Neural Machine Translation. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_5_1","volume-title":"Understanding the Role of Individual Units in a Deep Neural Network. PNAS","author":"Bau David","year":"2020","unstructured":"David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba. 2020. Understanding the Role of Individual Units in a Deep Neural Network. PNAS (2020)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"e_1_3_2_2_7_1","volume-title":"How Interpretable and Trustworthy Are GAMs? KDD","author":"Chang Chun-Hao","year":"2021","unstructured":"Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, and Rich Caruana. 2021. How Interpretable and Trustworthy Are GAMs? KDD (2021)."},{"key":"e_1_3_2_2_8_1","volume-title":"Kramer","author":"Croswell Jennifer M.","year":"2010","unstructured":"Jennifer M. Croswell, David F. Ransohoff, and Barnett S. Kramer. 2010. Principles of Cancer Screening: Lessons From History and Study Design Issues. Seminars in Oncology (2010)."},{"key":"e_1_3_2_2_9_1","volume-title":"Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project. Journal of Statistics Education","author":"Cock Dean De","year":"2011","unstructured":"Dean De Cock. 2011. Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project. Journal of Statistics Education (2011)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2016.2643"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2019.1629942"},{"key":"e_1_3_2_2_12_1","unstructured":"Trevor Hastie and Robert Tibshirani. 1999. Generalized Additive Models."},{"key":"e_1_3_2_2_13_1","volume-title":"Drucker","author":"Hohman Fred","year":"2019","unstructured":"Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M. Drucker. 2019. Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. CHI (2019)."},{"key":"e_1_3_2_2_14_1","volume-title":"TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning","author":"Hohman Fred","unstructured":"Fred Hohman, Arjun Srinivasan, and Steven Drucker. 2019. TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning. In IEEE VIS."},{"key":"e_1_3_2_2_15_1","volume-title":"Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. CSCW","author":"Hong Sungsoo Ray","year":"2020","unstructured":"Sungsoo Ray Hong, Jessica Hullman, and Enrico Bertini. 2020. Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. CSCW (2020)."},{"key":"e_1_3_2_2_16_1","volume-title":"Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. In CHI.","author":"Kaur Harmanpreet","year":"2020","unstructured":"Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020. Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. In CHI."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939874"},{"key":"e_1_3_2_2_18_1","volume-title":"Intelligible Models for Classification and Regression. KDD","author":"Lou Yin","year":"2012","unstructured":"Yin Lou, Rich Caruana, and Johannes Gehrke. 2012. Intelligible Models for Classification and Regression. KDD (2012)."},{"key":"e_1_3_2_2_19_1","volume-title":"Accurate Intelligible Models with Pairwise Interactions. KDD","author":"Lou Yin","year":"2013","unstructured":"Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013. Accurate Intelligible Models with Pairwise Interactions. KDD (2013)."},{"key":"e_1_3_2_2_20_1","volume-title":"Lundberg and Su-In Lee","author":"Scott","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. NeurIPS (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv","author":"Nori Harsha","year":"2019","unstructured":"Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv (2019)."},{"key":"e_1_3_2_2_22_1","volume-title":"Explaining the Predictions of Any Classifier. KDD","author":"Ribeiro Marco Tulio","year":"2016","unstructured":"Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. KDD (2016)."},{"key":"e_1_3_2_2_23_1","first-page":"8","article-title":"Direct Manipulation","volume":"16","author":"Shneiderman Ben","year":"1983","unstructured":"Ben Shneiderman. 1983. Direct Manipulation: A Step Beyond Programming Languages. Computer 16, 8 (Aug. 1983), 57--69.","journal-title":"A Step Beyond Programming Languages. Computer"},{"key":"e_1_3_2_2_24_1","volume-title":"Finding Experts in Transformer Models. arXiv","author":"Suau Xavier","year":"2020","unstructured":"Xavier Suau, Luca Zappella, and Nicholas Apostoloff. 2020. Finding Experts in Transformer Models. arXiv (2020)."},{"key":"e_1_3_2_2_25_1","volume-title":"Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics. VAST","author":"Wall Emily","year":"2017","unstructured":"Emily Wall, Leslie M. Blaha, Lyndsey Franklin, and Alex Endert. 2017. Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics. VAST (2017)."},{"key":"e_1_3_2_2_26_1","unstructured":"Caroline Wang Bin Han Bhrij Patel Feroze Mohideen and Cynthia Rudin. 2020. In Pursuit of Interpretable Fair and Accurate Machine Learning for Criminal Recidivism Prediction. arXiv (2020)."},{"key":"e_1_3_2_2_27_1","volume-title":"Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana.","author":"Wang Zijie J.","year":"2021","unstructured":"Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana. 2021. GAM Changer: Editing Generalized Additive Models with Interactive Visualization. arXiv (2021)."},{"key":"e_1_3_2_2_28_1","volume-title":"The What-If Tool: Interactive Probing of Machine Learning Models. TVCG","author":"Wexler James","year":"2019","unstructured":"James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, and Jimbo Wilson. 2019. The What-If Tool: Interactive Probing of Machine Learning Models. TVCG (2019)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Jiaming Zeng Berk Ustun and Cynthia Rudin. 2017. Interpretable Classification Models for Recidivism Prediction. In Journal of the Royal Statistical Society.","DOI":"10.1111\/rssa.12227"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539074","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539074","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:50Z","timestamp":1750183790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":29,"alternative-id":["10.1145\/3534678.3539074","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539074","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}