{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:58:17Z","timestamp":1776113897959,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,26]]},"DOI":"10.1145\/3706599.3720275","type":"proceedings-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T20:07:11Z","timestamp":1745438831000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Understanding Industry Practitioners' Experiences in Generative AI Governance"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8297-6792","authenticated-orcid":false,"given":"Hyo Jin","family":"Do","sequence":"first","affiliation":[{"name":"IBM Research, Cambridge, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6564-9683","authenticated-orcid":false,"given":"Swati","family":"Babbar","sequence":"additional","affiliation":[{"name":"IBM India Software Labs, Kochi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7666-5295","authenticated-orcid":false,"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[{"name":"IBM, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4326-0665","authenticated-orcid":false,"given":"Laura","family":"Walks","sequence":"additional","affiliation":[{"name":"IBM, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5488-1937","authenticated-orcid":false,"given":"Shayenna","family":"Misko","sequence":"additional","affiliation":[{"name":"IBM Software, B\u00f6blingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642016"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Matthew Arnold Rachel\u00a0KE Bellamy Michael Hind Stephanie Houde Sameep Mehta Aleksandra Mojsilovi\u0107 Ravi Nair K\u00a0Natesan Ramamurthy Alexandra Olteanu David Piorkowski et\u00a0al. 2019. FactSheets: Increasing trust in AI services through supplier\u2019s declarations of conformity. IBM Journal of Research and Development 63 4\/5 (2019) 6\u20131.","DOI":"10.1147\/JRD.2019.2942288"},{"key":"e_1_3_3_2_4_2","unstructured":"National Institute of\u00a0Standards Artificial Intelligence\u00a0(AI) and Technology (NIST). 2024. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile."},{"key":"e_1_3_3_2_5_2","unstructured":"National Institute of\u00a0Standards Artificial Intelligence\u00a0(AI) and Technology (NIST). 2024. NIST AI RMF Playbook. Available at https:\/\/airc.nist.gov\/AI_RMF_Knowledge_Base\/Playbook (last accessed: 2025\/01\/23)."},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3 2 (2006) 77\u2013101.","DOI":"10.1191\/1478088706qp063oa"},{"key":"e_1_3_3_2_7_2","unstructured":"Kasia Chmielinski Sarah Newman Chris\u00a0N. Kranzinger Michael Hind Jennifer\u00a0Wortman Vaughan Margaret Mitchell Julia Stoyanovich Angelina McMillan-Major Emily McReynolds Kathleen Esfahany Mary\u00a0L. Gray Audrey Chang and Maui Hudson. 2024. The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers. The Shorenstein Center on Media Politics and Public Policy (2024)."},{"key":"e_1_3_3_2_8_2","unstructured":"European Commission. [n. d.]. laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008 (EU) No 167\/2013 (EU) No 168\/2013 (EU) 2018\/858 (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act). http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Nicholas\u00a0Kluge Corr\u00eaa Camila Galv\u00e3o James\u00a0William Santos Carolina Del\u00a0Pino Edson\u00a0Pontes Pinto Camila Barbosa Diogo Massmann Rodrigo Mambrini Luiza Galv\u00e3o Edmund Terem et\u00a0al. 2023. Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns 4 10 (2023).","DOI":"10.1016\/j.patter.2023.100857"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533108"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer\u00a0Wortman Vaughan Hanna Wallach Hal\u00a0Daum\u00e9 Iii and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM 64 12 (2021) 86\u201392.","DOI":"10.1145\/3458723"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Michael Guihot Anne\u00a0F Matthew and Nicolas\u00a0P Suzor. 2017. Nudging robots: Innovative solutions to regulate artificial intelligence. Vand. J. Ent. & Tech. L. 20 (2017) 385.","DOI":"10.31228\/osf.io\/5at2f"},{"key":"e_1_3_3_2_13_2","unstructured":"Dan Hendrycks Mantas Mazeika and Thomas Woodside. 2023. An overview of catastrophic ai risks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2306.12001 (2023)."},{"key":"e_1_3_3_2_14_2","unstructured":"Lei Huang Weijiang Yu Weitao Ma Weihong Zhong Zhangyin Feng Haotian Wang Qianglong Chen Weihua Peng Xiaocheng Feng Bing Qin et\u00a0al. 2023. A survey on hallucination in large language models: Principles taxonomy challenges and open questions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2311.05232 (2023)."},{"key":"e_1_3_3_2_15_2","unstructured":"Michael Katell Meg Young Bernease Herman Dharma Dailey Aaron Tam Vivian Guetler Corinne Binz Daniella Raz and PM Krafft. 2019. An algorithmic equity toolkit for technology audits by community advocates and activists. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1912.02943 (2019)."},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642849"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Klaus Krippendorff. 2004. Reliability in content analysis: Some common misconceptions and recommendations. Human communication research 30 3 (2004) 411\u2013433.","DOI":"10.1093\/hcr\/30.3.411"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Stefan Larsson. 2020. On the governance of artificial intelligence through ethics guidelines. Asian Journal of Law and Society 7 3 (2020) 437\u2013451.","DOI":"10.1017\/als.2020.19"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3603555.3603565"},{"key":"e_1_3_3_2_20_2","first-page":"6565","volume-title":"International Conference on Machine Learning","author":"Liang Paul\u00a0Pu","year":"2021","unstructured":"Paul\u00a0Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning. PMLR, 6565\u20136576."},{"key":"e_1_3_3_2_21_2","unstructured":"Q\u00a0Vera Liao Milena Pribi\u0107 Jaesik Han Sarah Miller and Daby Sow. 2021. Question-driven design process for explainable AI user experiences. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2104.03483 (2021)."},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Q.\u00a0Vera Liao and Jennifer Wortman\u00a0Vaughan. 2024. AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap. Harvard Data Science ReviewSpecial Issue 5 (may 31 2024). https:\/\/hdsr.mitpress.mit.edu\/pub\/aelql9qy.","DOI":"10.1162\/99608f92.8036d03b"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Qinghua Lu Liming Zhu Xiwei Xu Jon Whittle Didar Zowghi and Aurelie Jacquet. 2024. Responsible AI pattern catalogue: A collection of best practices for AI governance and engineering. Comput. Surveys 56 7 (2024) 1\u201335.","DOI":"10.1145\/3626234"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22668-2_19"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376445"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019) 1\u201338.","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Brent\u00a0D Mittelstadt. 2019. AI ethics-too principled to fail? CoRR (2019).","DOI":"10.2139\/ssrn.3391293"},{"key":"e_1_3_3_2_29_2","unstructured":"OECD. [n. d.]. The RAISE Corporate AI Policy Benchmarks. ([n. d.]). https:\/\/oecd.ai\/en\/catalogue\/tools\/raise-benchmarks Uploaded on Dec 14 2023."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533231"},{"key":"e_1_3_3_2_31_2","unstructured":"Dillon Reisman Jason Schultz Kate Crawford and Meredith Whittaker. 2018. Algorithmic impact assessments: a practical Framework for Public Agency. AI Now 9 (2018)."},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Ben Shneiderman. 2020. Bridging the gap between ethics and practice: guidelines for reliable safe and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 10 4 (2020) 1\u201331.","DOI":"10.1145\/3419764"},{"key":"e_1_3_3_2_33_2","unstructured":"Catharina\u00a0Doria Susannah\u00a0Shattuck Ian\u00a0Eisenberg. 2022. What Is AI Governance and Why Should You Care? Available at https:\/\/www.credo.ai\/blog\/cutting-through-the-noise-what-is-ai-governance (last accessed: 2025\/01\/22)."},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Elham Tabassi. 2023. Artificial intelligence risk management framework (AI RMF 1.0). (2023).","DOI":"10.6028\/NIST.AI.100-1"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Araz Taeihagh. 2021. Governance of artificial intelligence. Policy and society 40 2 (2021) 137\u2013157.","DOI":"10.1080\/14494035.2021.1928377"},{"key":"e_1_3_3_2_36_2","unstructured":"Margareth Theresia. 2024. Newly enacted law sets basis for nat\u2019l development of AI. Available at https:\/\/www.korea.net\/NewsFocus\/policies\/view?articleId=264071 (last accessed: 2025\/01\/23)."},{"key":"e_1_3_3_2_37_2","unstructured":"Unknown. 2025. Top 8 AI Governance Platforms for 2025. Available at https:\/\/www.domo.com\/learn\/article\/ai-governance-tools (last accessed: 2025\/01\/23)."},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642335"},{"key":"e_1_3_3_2_39_2","unstructured":"IBM Research AI FactSheets\u00a0360 Website. [n. d.]. AI Lifecycle Governance. https:\/\/aifs360.res.ibm.com\/governance"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Bernd\u00a0W Wirtz Jan\u00a0C Weyerer and Benjamin\u00a0J Sturm. 2020. The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration 43 9 (2020) 818\u2013829.","DOI":"10.1080\/01900692.2020.1749851"},{"key":"e_1_3_3_2_41_2","unstructured":"Hazal \u015eim\u015fek. 2025. Compare Top 25 AI Governance Tools: A Vendor Benchmark [2025]. Available at https:\/\/research.aimultiple.com\/ai-governance-tools\/ (last accessed: 2025\/01\/22)."}],"event":{"name":"CHI EA '25: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","location":"Yokohama Japan","acronym":"CHI EA '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706599.3720275","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706599.3720275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:51Z","timestamp":1750295931000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706599.3720275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":40,"alternative-id":["10.1145\/3706599.3720275","10.1145\/3706599"],"URL":"https:\/\/doi.org\/10.1145\/3706599.3720275","relation":{},"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"2025-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}