{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:05:35Z","timestamp":1781006735049,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":143,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1850195"],"award-info":[{"award-number":["IIS-1850195"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3772318.3791347","type":"proceedings-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:12:26Z","timestamp":1776053546000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3886-8687","authenticated-orcid":false,"given":"Jing Nathan","family":"Yan","sequence":"first","affiliation":[{"name":"CIS, Cornell University, Ithaca, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8453-4963","authenticated-orcid":false,"given":"Emma","family":"Harvey","sequence":"additional","affiliation":[{"name":"Information Science, Cornell University, Ithaca, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6808-8176","authenticated-orcid":false,"given":"Junxiong","family":"Wang","sequence":"additional","affiliation":[{"name":"CIS, Cornell University, Ithaca, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-9501","authenticated-orcid":false,"given":"Jeffrey M","family":"Rzeszotarski","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Loyola University Maryland, Baltimore, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6233-8256","authenticated-orcid":false,"given":"Allison","family":"Koenecke","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_3_3_3_2_2","unstructured":"Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1907.13158 (2019)."},{"key":"e_1_3_3_3_3_2","unstructured":"Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1901.07555 (2019)."},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"publisher","unstructured":"Muhammad Ali Piotr Sapiezynski Miranda Bogen Aleksandra Korolova Alan Mislove and Aaron Rieke. 2019. Discrimination through Optimization: How Facebook\u2019s Ad Delivery Can Lead to Biased Outcomes. Proc. ACM Hum.-Comput. Interact. 3 CSCW Article 199 (Nov. 2019) 30\u00a0pages. 10.1145\/3359301","DOI":"10.1145\/3359301"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3593990"},{"key":"e_1_3_3_3_6_2","doi-asserted-by":"publisher","unstructured":"Henk Alkemade Steven Claeyssens Giovanni Colavizza Nuno Freire J\u00f6rg Lehmann Clemens Neudecker Giulia Osti and Daniel van Strien. 2023. Datasheets for Digital Cultural Heritage Datasets. Journal of Open Humanities Data 9 (2023). 10.5334\/johd.124","DOI":"10.5334\/johd.124"},{"key":"e_1_3_3_3_7_2","doi-asserted-by":"publisher","unstructured":"Anitha Anandhan Liyana Shuib Maizatul\u00a0Akmar Ismail and Ghulam Mujtaba. 2018. Social Media Recommender Systems: Review and Open Research Issues. IEEE Access 6 (2018) 15608\u201315628. 10.1109\/ACCESS.2018.2810062","DOI":"10.1109\/ACCESS.2018.2810062"},{"key":"e_1_3_3_3_8_2","unstructured":"Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine Bias. ProPublica. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"e_1_3_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.5"},{"key":"e_1_3_3_3_10_2","unstructured":"Matthew Arnold Rachel K.\u00a0E. Bellamy Michael Hind Stephanie Houde Sameep Mehta Aleksandra Mojsilovic Ravi Nair Karthikeyan\u00a0Natesan Ramamurthy Darrell Reimer Alexandra Olteanu David Piorkowski Jason Tsay and Kush\u00a0R. Varshney. 2019. FactSheets: Increasing Trust in AI Services through Supplier\u2019s Declarations of Conformity. arxiv:https:\/\/arXiv.org\/abs\/1808.07261\u00a0[cs.CY] https:\/\/arxiv.org\/abs\/1808.07261"},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300079"},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3600211.3604674"},{"key":"e_1_3_3_3_13_2","doi-asserted-by":"crossref","unstructured":"Solon Barocas and Andrew\u00a0D Selbst. 2016. Big data\u2019s disparate impact. Calif. L. Rev. 104 (2016) 671.","DOI":"10.2139\/ssrn.2477899"},{"key":"e_1_3_3_3_14_2","unstructured":"Luca Belli Sofia\u00a0Ira Ktena Alykhan Tejani Alexandre Lung-Yut-Fon Frank Portman Xiao Zhu Yuanpu Xie Akshay Gupta Michael Bronstein Amra Deli\u0107 et\u00a0al. 2020. Privacy-Aware Recommender Systems Challenge on Twitter\u2019s Home Timeline. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2004.13715 (2020)."},{"key":"e_1_3_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_3_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330745"},{"key":"e_1_3_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314234"},{"key":"e_1_3_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159727"},{"key":"e_1_3_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210063"},{"key":"e_1_3_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3617694.3623259"},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.485"},{"key":"e_1_3_3_3_22_2","doi-asserted-by":"publisher","unstructured":"Margarita Boenig-Liptsin Anissa Tanweer and Ari Edmundson. 2022. Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice. Journal of Statistics and Data Science Education 30 3 (2022) 228\u2013240. arXiv:10.1080\/26939169.2022.208941110.1080\/26939169.2022.2089411","DOI":"10.1080\/26939169.2022.2089411"},{"key":"e_1_3_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534626"},{"key":"e_1_3_3_3_24_2","doi-asserted-by":"publisher","unstructured":"Karen\u00a0L. Boyd. 2021. Datasheets for Datasets help ML Engineers Notice and Understand Ethical Issues in Training Data. Proc. ACM Hum.-Comput. Interact. 5 CSCW2 Article 438 (Oct. 2021) 27\u00a0pages. 10.1145\/3479582","DOI":"10.1145\/3479582"},{"key":"e_1_3_3_3_25_2","doi-asserted-by":"publisher","unstructured":"Karen\u00a0L. Boyd and Katie Shilton. 2021. Adapting Ethical Sensitivity as a Construct to Study Technology Design Teams. Proc. ACM Hum.-Comput. Interact. 5 GROUP Article 217 (July 2021) 29\u00a0pages. 10.1145\/3463929","DOI":"10.1145\/3463929"},{"key":"e_1_3_3_3_26_2","doi-asserted-by":"publisher","unstructured":"Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3 2 (Jan. 2006) 77\u2013101. 10.1191\/1478088706qp063oa","DOI":"10.1191\/1478088706qp063oa"},{"key":"e_1_3_3_3_27_2","unstructured":"Robin Burke. 2017. Multisided fairness for recommendation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1707.00093 (2017)."},{"key":"e_1_3_3_3_28_2","first-page":"202","volume-title":"Conference on fairness, accountability and transparency","author":"Burke Robin","year":"2018","unstructured":"Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018. Balanced neighborhoods for multi-sided fairness in recommendation. In Conference on fairness, accountability and transparency. PMLR, 202\u2013214."},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858498"},{"key":"e_1_3_3_3_30_2","doi-asserted-by":"crossref","unstructured":"Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41 3 (2023) 1\u201339.","DOI":"10.1145\/3564284"},{"key":"e_1_3_3_3_31_2","doi-asserted-by":"crossref","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5 2 (2017) 153\u2013163.","DOI":"10.1089\/big.2016.0047"},{"key":"e_1_3_3_3_32_2","unstructured":"Sam Corbett-Davies Johann\u00a0D Gaebler Hamed Nilforoshan Ravi Shroff and Sharad Goel. 2023. The measure and mismeasure of fairness. Journal of Machine Learning Research 24 312 (2023) 1\u2013117."},{"key":"e_1_3_3_3_33_2","doi-asserted-by":"publisher","unstructured":"Isabel Corpus Eric Giannella Allison Koenecke and Don Moynihan. 2025. As Government Outsources More IT Highly Skilled In-House Technologists Are More Essential. Commun. ACM 68 7 (June 2025) 37\u201340. 10.1145\/3727635","DOI":"10.1145\/3727635"},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533213"},{"key":"e_1_3_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290607.3299057"},{"key":"e_1_3_3_3_36_2","doi-asserted-by":"publisher","unstructured":"Fernando Delgado Solon Barocas and Karen Levy. 2022. An Uncommon Task: Participatory Design in Legal AI. Proceedings of the ACM on Human-Computer Interaction 6 CSCW1 (March 2022) 1\u201323. 10.1145\/3512898","DOI":"10.1145\/3512898"},{"key":"e_1_3_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581026"},{"key":"e_1_3_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533113"},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594037"},{"key":"e_1_3_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025659"},{"key":"e_1_3_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411962"},{"key":"e_1_3_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-2197-4_18"},{"key":"e_1_3_3_3_44_2","series-title":"Proceedings of Machine Learning Research","first-page":"172","volume-title":"Proceedings of the 1st Conference on Fairness, Accountability and Transparency","volume":"81","author":"Ekstrand Michael\u00a0D.","year":"2018","unstructured":"Michael\u00a0D. Ekstrand, Mucun Tian, Ion\u00a0Madrazo Azpiazu, Jennifer\u00a0D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria\u00a0Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency(Proceedings of Machine Learning Research, Vol.\u00a081), Sorelle\u00a0A. Friedler and Christo Wilson (Eds.). PMLR, 172\u2013186. https:\/\/proceedings.mlr.press\/v81\/ekstrand18b.html"},{"key":"e_1_3_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858494"},{"key":"e_1_3_3_3_46_2","doi-asserted-by":"publisher","unstructured":"Yunhe Feng and Chirag Shah. 2022. Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search. Proceedings of the AAAI Conference on Artificial Intelligence 36 11 (June 2022) 11882\u201311890. 10.1609\/aaai.v36i11.21445","DOI":"10.1609\/aaai.v36i11.21445"},{"key":"e_1_3_3_3_47_2","unstructured":"Mila Fiordalisi. 2025. The FTC Warns Big Tech Companies Not to Apply the Digital Services Act. Wired. https:\/\/www.wired.com\/story\/big-tech-companies-in-the-us-have-been-told-not-to-apply-the-digital-services-act\/"},{"key":"e_1_3_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7585.001.0001"},{"key":"e_1_3_3_3_49_2","doi-asserted-by":"publisher","unstructured":"Bhargavi Ganesh Daniel\u00a0S. Schiff and Stuart Anderson. 2025. The \u2018Wild West\u2019 of Medicine: Exploring the Emergence of \u2018Grassroots\u2019 AI Governance in Radiology. Proceedings of the AAAI\/ACM Conference on AI Ethics and Society 8 2 (Oct. 2025) 1018\u20131031. 10.1609\/aies.v8i2.36608","DOI":"10.1609\/aies.v8i2.36608"},{"key":"e_1_3_3_3_50_2","unstructured":"Jean Garcia-Gathright Aaron Springer and Henriette Cramer. 2018. Assessing and Addressing Algorithmic Bias - But Before We Get There. http:\/\/arxiv.org\/abs\/1809.03332 arXiv:https:\/\/arXiv.org\/abs\/1809.03332 [cs]."},{"key":"e_1_3_3_3_51_2","doi-asserted-by":"publisher","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 (Dec. 2021) 86\u201392. 10.1145\/3458723","DOI":"10.1145\/3458723"},{"key":"e_1_3_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_3_3_53_2","doi-asserted-by":"crossref","unstructured":"Ulrike Gretzel and Daniel\u00a0R Fesenmaier. 2006. Persuasion in recommender systems. International Journal of Electronic Commerce 11 2 (2006) 81\u2013100.","DOI":"10.2753\/JEC1086-4415110204"},{"key":"e_1_3_3_3_54_2","series-title":"(NIPS)","volume-title":"Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems","author":"Grgic-Hlaca Nina","year":"2016","unstructured":"Nina Grgic-Hlaca, M.\u00a0B. Zafar, K. Gummadi, and Adrian Weller. 2016. The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making. In Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems(NIPS). Barcelona, Spain. https:\/\/www.semanticscholar.org\/paper\/The-Case-for-Process-Fairness-in-Learning%3A-Feature-Grgic-Hlaca-Zafar\/fdb6a159cb65f4d1147224998d56e67f0398948b"},{"key":"e_1_3_3_3_55_2","unstructured":"Luke Guerdan Devansh Saxena Stevie Chancellor Zhiwei\u00a0Steven Wu and Kenneth Holstein. 2025. Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks. arxiv:https:\/\/arXiv.org\/abs\/2507.02819\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2507.02819"},{"key":"e_1_3_3_3_56_2","unstructured":"Emma Harvey Emily Sheng Su\u00a0Lin Blodgett Alexandra Chouldechova Jean Garcia-Gathright Alexandra Olteanu and Hanna Wallach. 2024. Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems. arxiv:https:\/\/arXiv.org\/abs\/2411.15662\u00a0[cs.CY] https:\/\/arxiv.org\/abs\/2411.15662"},{"key":"e_1_3_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.947"},{"key":"e_1_3_3_3_58_2","doi-asserted-by":"crossref","unstructured":"Amy\u00a0K Heger Liz\u00a0B Marquis Mihaela Vorvoreanu Hanna Wallach and Jennifer Wortman\u00a0Vaughan. 2022. Understanding machine learning practitioners\u2019 data documentation perceptions needs challenges and desiderata. Proceedings of the ACM on Human-Computer Interaction 6 CSCW2 (2022) 1\u201329.","DOI":"10.1145\/3555760"},{"key":"e_1_3_3_3_59_2","doi-asserted-by":"publisher","unstructured":"Mireille Hildebrandt. 2022. The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems. Frontiers in Artificial Intelligence Volume 5 - 2022 (2022). 10.3389\/frai.2022.789076","DOI":"10.3389\/frai.2022.789076"},{"key":"e_1_3_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300830"},{"key":"e_1_3_3_3_61_2","doi-asserted-by":"publisher","unstructured":"Ferenc Husz\u00e1r Sofia\u00a0Ira Ktena Conor O\u2019Brien Luca Belli Andrew Schlaikjer and Moritz Hardt. 2022. Algorithmic amplification of politics on Twitter. Proceedings of the National Academy of Sciences 119 1 (2022) e2025334119. 10.1073\/pnas.2025334119","DOI":"10.1073\/pnas.2025334119"},{"key":"e_1_3_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287600"},{"key":"e_1_3_3_3_63_2","doi-asserted-by":"publisher","unstructured":"Jevan\u00a0A. Hutson Jessie\u00a0G. Taft Solon Barocas and Karen Levy. 2018. Debiasing Desire: Addressing Bias & Discrimination on Intimate Platforms. Proc. ACM Hum.-Comput. Interact. 2 CSCW Article 73 (Nov. 2018) 18\u00a0pages. 10.1145\/3274342","DOI":"10.1145\/3274342"},{"key":"e_1_3_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445901"},{"key":"e_1_3_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3641902"},{"key":"e_1_3_3_3_66_2","doi-asserted-by":"publisher","unstructured":"Deborah\u00a0Dormah Kanubala and Isabel Valera. 2025. On the Misalignment Between Legal Notions and Statistical Metrics of Intersectional Fairness. Proceedings of the AAAI\/ACM Conference on AI Ethics and Society 8 2 (Oct. 2025) 1363\u20131374. 10.1609\/aies.v8i2.36637","DOI":"10.1609\/aies.v8i2.36637"},{"key":"e_1_3_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376219"},{"key":"e_1_3_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287592"},{"key":"e_1_3_3_3_70_2","unstructured":"Daphne Keller. 2021. Amplification and its discontents: Why regulating the reach of online content is hard. J. FREE SPEECH L. 1 (2021) 227\u2013268."},{"key":"e_1_3_3_3_71_2","doi-asserted-by":"publisher","unstructured":"Jon Kleinberg Sendhil Mullainathan and Manish Raghavan. 2017. Inherent Trade-Offs in the Fair Determination of Risk Scores. Leibniz International Proceedings in Informatics (2017). 10.4230\/LIPICS.ITCS.2017.43","DOI":"10.4230\/LIPICS.ITCS.2017.43"},{"key":"e_1_3_3_3_72_2","doi-asserted-by":"publisher","unstructured":"Allison Koenecke Eric Giannella Robb Willer and Sharad Goel. 2023. Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness. Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2023) 494\u2013506. 10.1609\/icwsm.v17i1.22163","DOI":"10.1609\/icwsm.v17i1.22163"},{"key":"e_1_3_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713155"},{"key":"e_1_3_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300717"},{"key":"e_1_3_3_3_75_2","unstructured":"Akos Lada Meihong Wang and Tak Yan. 2021. How machine learning powers Facebook\u2019s News Feed ranking algorithm. Facebook Engineering (2021)."},{"key":"e_1_3_3_3_76_2","doi-asserted-by":"publisher","unstructured":"Min\u00a0Kyung Lee Daniel Kusbit Anson Kahng Ji\u00a0Tae Kim Xinran Yuan Allissa Chan Daniel See Ritesh Noothigattu Siheon Lee Alexandros Psomas and Ariel\u00a0D. Procaccia. 2019. WeBuildAI: Participatory Framework for Algorithmic Governance. Proceedings of the ACM on Human-Computer Interaction 3 CSCW (Nov. 2019) 1\u201335. 10.1145\/3359283","DOI":"10.1145\/3359283"},{"key":"e_1_3_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445261"},{"key":"e_1_3_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186949"},{"key":"e_1_3_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713241"},{"key":"e_1_3_3_3_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449866"},{"key":"e_1_3_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462814"},{"key":"e_1_3_3_3_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533105"},{"key":"e_1_3_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3502049"},{"key":"e_1_3_3_3_84_2","doi-asserted-by":"crossref","unstructured":"Michael Madaio Lisa Egede Hariharan Subramonyam Jennifer Wortman\u00a0Vaughan and Hanna Wallach. 2022. Assessing the Fairness of AI Systems: AI Practitioners\u2019 Processes Challenges and Needs for Support. Proceedings of the ACM on Human-Computer Interaction 6 CSCW1 (2022) 1\u201326.","DOI":"10.1145\/3512899"},{"key":"e_1_3_3_3_85_2","doi-asserted-by":"publisher","unstructured":"Michael\u00a0A. Madaio Jingya Chen Hanna Wallach and Jennifer Wortman\u00a0Vaughan. 2024. Tinker Tailor Configure Customize: The Articulation Work of Contextualizing an AI Fairness Checklist. Proc. ACM Hum.-Comput. Interact. 8 CSCW1 Article 214 (April 2024) 20\u00a0pages. 10.1145\/3653705","DOI":"10.1145\/3653705"},{"key":"e_1_3_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376445"},{"key":"e_1_3_3_3_87_2","unstructured":"Tambiama Madiega. 2020. Digital services act. European Parliamentary Research Service PE (2020)."},{"key":"e_1_3_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-41264-6_8"},{"key":"e_1_3_3_3_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272027"},{"key":"e_1_3_3_3_90_2","doi-asserted-by":"publisher","unstructured":"Jacob Metcalf Emanuel Moss and Danah Boyd. 2019. Owning Ethics: Corporate Logics Silicon Valley and the Institutionalization of Ethics. Social Research: An International Quarterly 86 2 (June 2019) 449\u2013476. 10.1353\/sor.2019.0022","DOI":"10.1353\/sor.2019.0022"},{"key":"e_1_3_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_3_3_92_2","unstructured":"Sanika Moharana Cynthia\u00a0L. Bennett Erin Buehler Michael Madaio Vinita Tibdewal and Shaun\u00a0K. Kane. 2025. \"Accessibility people you go work on that thing of yours over there\": Addressing Disability Inclusion in AI Product Organizations. arxiv:https:\/\/arXiv.org\/abs\/2508.16607\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2508.16607"},{"key":"e_1_3_3_3_93_2","doi-asserted-by":"publisher","unstructured":"Jessica Morley Anat Elhalal Francesca Garcia Libby Kinsey Jakob M\u00f6kander and Luciano Floridi. 2021. Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. Minds and Machines 31 2 (01 Jun 2021) 239\u2013256. 10.1007\/s11023-021-09563-w","DOI":"10.1007\/s11023-021-09563-w"},{"key":"e_1_3_3_3_94_2","doi-asserted-by":"publisher","unstructured":"Jessica Morley Luciano Floridi Libby Kinsey and Anat Elhalal. 2020. From What to How: An Initial Review of Publicly Available AI Ethics Tools Methods and Research to Translate Principles into Practices. Science and Engineering Ethics 26 4 (01 Aug 2020) 2141\u20132168. 10.1007\/s11948-019-00165-5","DOI":"10.1007\/s11948-019-00165-5"},{"key":"e_1_3_3_3_95_2","doi-asserted-by":"publisher","unstructured":"Jessica Morley Libby Kinsey Anat Elhalal Francesca Garcia Marta Ziosi and Luciano Floridi. 2023. Operationalising AI ethics: barriers enablers and next steps. AI & SOCIETY 38 1 (Feb. 2023) 411\u2013423. 10.1007\/s00146-021-01308-8","DOI":"10.1007\/s00146-021-01308-8"},{"key":"e_1_3_3_3_96_2","first-page":"81","volume-title":"Proceedings of the AAAI workshop on recommender systems","volume":"83","author":"Oard Douglas\u00a0W","year":"1998","unstructured":"Douglas\u00a0W Oard, Jinmook Kim, et\u00a0al. 1998. Implicit feedback for recommender systems. In Proceedings of the AAAI workshop on recommender systems , Vol.\u00a083. Madison, WI, 81\u201383."},{"key":"e_1_3_3_3_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713301"},{"key":"e_1_3_3_3_98_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594098"},{"key":"e_1_3_3_3_99_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594049"},{"key":"e_1_3_3_3_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287567"},{"key":"e_1_3_3_3_101_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533238"},{"key":"e_1_3_3_3_102_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594075"},{"key":"e_1_3_3_3_103_2","doi-asserted-by":"crossref","unstructured":"Bogdana Rakova Jingying Yang Henriette Cramer and Rumman Chowdhury. 2021. Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer Interaction 5 CSCW1 (2021) 1\u201323.","DOI":"10.1145\/3449081"},{"key":"e_1_3_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.1145\/3689904.3694703"},{"key":"e_1_3_3_3_105_2","doi-asserted-by":"publisher","DOI":"10.4159\/9780674042582"},{"key":"e_1_3_3_3_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445604"},{"key":"e_1_3_3_3_107_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533140"},{"key":"e_1_3_3_3_108_2","doi-asserted-by":"publisher","unstructured":"Samantha Robertson Tonya Nguyen and Niloufar Salehi. 2022. Not Another School Resource Map: Meeting Underserved Families\u2019 Information Needs Requires Trusting Relationships and Personalized Care. Proceedings of the ACM on Human-Computer Interaction 6 CSCW2 (Nov. 2022) 1\u201323. 10.1145\/3555207","DOI":"10.1145\/3555207"},{"key":"e_1_3_3_3_109_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445518"},{"key":"e_1_3_3_3_110_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658918"},{"key":"e_1_3_3_3_111_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533110"},{"key":"e_1_3_3_3_112_2","doi-asserted-by":"crossref","unstructured":"Judith Simon Pak-Hang Wong and Gernot Rieder. 2020. Algorithmic bias and the Value Sensitive Design approach. Internet Pol. Rev. 9 4 (Dec. 2020).","DOI":"10.14763\/2020.4.1534"},{"key":"e_1_3_3_3_113_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_3_3_114_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533122"},{"key":"e_1_3_3_3_115_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583204"},{"key":"e_1_3_3_3_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594106"},{"key":"e_1_3_3_3_117_2","doi-asserted-by":"publisher","DOI":"10.1145\/3715275.3732040"},{"key":"e_1_3_3_3_118_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3659044"},{"key":"e_1_3_3_3_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450613.3456835"},{"key":"e_1_3_3_3_120_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240372"},{"key":"e_1_3_3_3_121_2","doi-asserted-by":"crossref","unstructured":"Jonathan Stray Alon Halevy Parisa Assar Dylan Hadfield-Menell Craig Boutilier Amar Ashar Chloe Bakalar Lex Beattie Michael Ekstrand Claire Leibowicz et\u00a0al. 2024. Building human values into recommender systems: An interdisciplinary synthesis. ACM Transactions on Recommender Systems 2 3 (2024) 1\u201357.","DOI":"10.1145\/3632297"},{"key":"e_1_3_3_3_122_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581123"},{"key":"e_1_3_3_3_123_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465416.3483305"},{"key":"e_1_3_3_3_124_2","doi-asserted-by":"publisher","unstructured":"Latanya Sweeney. 2013. Discrimination in online ad delivery. Commun. ACM 56 5 (May 2013) 44\u201354. 10.1145\/2447976.2447990","DOI":"10.1145\/2447976.2447990"},{"key":"e_1_3_3_3_125_2","unstructured":"Ryan Tate. 2023. Amazon Ranks Its Own Products First FTC Lawsuit Says. The Markup. https:\/\/themarkup.org\/amazons-advantage\/2023\/09\/28\/amazon-ranks-its-own-products-first-ftc-lawsuit-says"},{"key":"e_1_3_3_3_126_2","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174014"},{"key":"e_1_3_3_3_127_2","doi-asserted-by":"publisher","DOI":"10.1145\/3715275.3732033"},{"key":"e_1_3_3_3_128_2","doi-asserted-by":"publisher","DOI":"10.1609\/aies.v7i1.31743"},{"key":"e_1_3_3_3_129_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533101"},{"key":"e_1_3_3_3_130_2","doi-asserted-by":"publisher","unstructured":"Clarice Wang Kathryn Wang Andrew\u00a0Y. Bian Rashidul Islam Kamrun\u00a0Naher Keya James Foulds and Shimei Pan. 2023. When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation. ACM Trans. Interact. Intell. Syst. 13 3 Article 17 (Sept. 2023) 28\u00a0pages. 10.1145\/3611313","DOI":"10.1145\/3611313"},{"key":"e_1_3_3_3_131_2","doi-asserted-by":"crossref","unstructured":"Shoujin Wang Xiuzhen Zhang Yan Wang and Francesco Ricci. 2024. Trustworthy recommender systems. ACM Transactions on Intelligent Systems and Technology 15 4 (2024) 1\u201320.","DOI":"10.1145\/3627826"},{"key":"e_1_3_3_3_132_2","doi-asserted-by":"publisher","unstructured":"David\u00a0Gray Widder Meredith Whittaker and Sarah\u00a0Myers West. 2024. Why \u2018open\u2019 AI systems are actually closed and why this matters. Nature 635 8040 (01 Nov 2024) 827\u2013833. 10.1038\/s41586-024-08141-1","DOI":"10.1038\/s41586-024-08141-1"},{"key":"e_1_3_3_3_133_2","doi-asserted-by":"publisher","unstructured":"Richmond\u00a0Y. Wong Michael\u00a0A. Madaio and Nick Merrill. 2023. Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics. Proceedings of the ACM on Human-Computer Interaction 7 CSCW1 (April 2023) 1\u201327. 10.1145\/3579621","DOI":"10.1145\/3579621"},{"key":"e_1_3_3_3_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658998"},{"key":"e_1_3_3_3_135_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713279"},{"key":"e_1_3_3_3_136_2","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3085526"},{"key":"e_1_3_3_3_137_2","doi-asserted-by":"publisher","DOI":"10.1145\/3715275.3732159"},{"key":"e_1_3_3_3_138_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3470825"},{"key":"e_1_3_3_3_139_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132938"},{"key":"e_1_3_3_3_140_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3380048"},{"key":"e_1_3_3_3_141_2","doi-asserted-by":"publisher","unstructured":"Meike Zehlike Ke Yang and Julia Stoyanovich. 2022. Fairness in Ranking Part I: Score-Based Ranking. ACM Comput. Surv. 55 6 Article 118 (Dec. 2022) 36\u00a0pages. 10.1145\/3533379","DOI":"10.1145\/3533379"},{"key":"e_1_3_3_3_142_2","doi-asserted-by":"publisher","unstructured":"Meike Zehlike Ke Yang and Julia Stoyanovich. 2022. Fairness in Ranking Part II: Learning-to-Rank and Recommender Systems. ACM Comput. Surv. 55 6 Article 117 (Dec. 2022) 41\u00a0pages. 10.1145\/3533380","DOI":"10.1145\/3533380"},{"key":"e_1_3_3_3_143_2","first-page":"325","volume-title":"International Conference on Machine Learning","author":"Zemel Rich","year":"2013","unstructured":"Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning fair representations. In International Conference on Machine Learning. 325\u2013333."},{"key":"e_1_3_3_3_144_2","doi-asserted-by":"publisher","unstructured":"Haiyi Zhu Bowen Yu Aaron Halfaker and Loren Terveen. 2018. Value-Sensitive Algorithm Design: Method Case Study and Lessons. Proc. ACM Hum.-Comput. Interact. 2 CSCW Article 194 (Nov. 2018) 23\u00a0pages. 10.1145\/3274463","DOI":"10.1145\/3274463"}],"event":{"name":"CHI 2026: CHI Conference on Human Factors in Computing Systems","location":"Barcelona Spain","acronym":"CHI '26","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3772318.3791347","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3772318.3791347","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3772318.3791347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:38:01Z","timestamp":1781005081000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3772318.3791347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":143,"alternative-id":["10.1145\/3772318.3791347","10.1145\/3772318"],"URL":"https:\/\/doi.org\/10.1145\/3772318.3791347","relation":{},"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"2026-04-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}