{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T18:10:04Z","timestamp":1755886204305,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Amazon Web Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3630106.3658923","type":"proceedings-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T09:14:21Z","timestamp":1717578861000},"page":"529-545","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Diversified Ensembling: An Experiment in Crowdsourced Machine Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0641-0591","authenticated-orcid":false,"given":"Ira","family":"Globus-Harris","sequence":"first","affiliation":[{"name":"Computer and Information Sciences, University of Pennsylvania, USA and Amazon Web Services Artificial Intelligence (AWS AI), USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0174-3474","authenticated-orcid":false,"given":"Declan","family":"Harrison","sequence":"additional","affiliation":[{"name":"Computer and Information Sciences, University of Pennsylvania, USA and Amazon Web Services Artificial Intelligence (AWS AI), USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7569-0147","authenticated-orcid":false,"given":"Michael","family":"Kearns","sequence":"additional","affiliation":[{"name":"Computer and Information Sciences, University of Pennsylvania, USA and Amazon Web Services Artificial Intelligence (AWS AI), USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7583-5809","authenticated-orcid":false,"given":"Pietro","family":"Perona","sequence":"additional","affiliation":[{"name":"California Institute of Technology, USA and Amazon Web Services Artificial Intelligence (AWS AI), USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0586-0515","authenticated-orcid":false,"given":"Aaron","family":"Roth","sequence":"additional","affiliation":[{"name":"Computer and Information Sciences, University of Pennsylvania, Amazon AWS AI, USA and Amazon Web Services Artificial Intelligence (AWS AI), USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"unstructured":"Benji Andrews Hema Natarajan Maggie Ron Ellis and Ryan Holbrook. 2023. Benetech - Making Graphs Accessible. https:\/\/kaggle.com\/competitions\/benetech-making-graphs-accessible","key":"e_1_3_2_1_1_1"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a037)","author":"Blum Avrim","year":"2015","unstructured":"Avrim Blum and Moritz Hardt. 2015. The Ladder: A Reliable Leaderboard for Machine Learning Competitions. In Proceedings of the 32nd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a037), Francis Bach and David Blei (Eds.). PMLR, Lille, France, 1006\u20131014. https:\/\/proceedings.mlr.press\/v37\/blum15.html"},{"unstructured":"Aaron Carman Alexander Heifler Ashley Chow and Ryan Holbrook. 2023. ICR - Identifying Age-Related Conditions. https:\/\/kaggle.com\/competitions\/icr-identify-age-related-conditions","key":"e_1_3_2_1_3_1"},{"unstructured":"US Census. October 20 2022. 2021 ACS PUMS Data Dictionary. Data Dictionary. US Census. https:\/\/www2.census.gov\/programs-surveys\/acs\/tech_docs\/pums\/data_dict\/PUMS_Data_Dictionary_2021.pdf","key":"e_1_3_2_1_4_1"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the KDD 2014 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chou Sophie","year":"2014","unstructured":"Sophie Chou, William Li, and Ramesh Sridharan. 2014. Democratizing data science. In Proceedings of the KDD 2014 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA. 24\u201327."},{"unstructured":"Ashley Chow Glenn Cameron Manfred Georg Mark Sherwood Phil Culliton Sam Sepah Sohier Dane and Thad Starner. 2023. Google - American Sign Language Fingerspelling Recognition. https:\/\/kaggle.com\/competitions\/asl-fingerspelling","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","volume-title":"Image Matching Challenge","author":"Chow Ashley","year":"2023","unstructured":"Ashley Chow, Eduard Trulls, Jevster, Kwang\u00a0Moo Yi, Sohier Dane, Tanji Gou, and Weiwei Sun. 2023. Image Matching Challenge 2023. https:\/\/kaggle.com\/competitions\/image-matching-challenge-2023"},{"unstructured":"Rumman Chowdhury and Jutta Williams. 2021. Introducing Twitter\u2019s First Algorithmic Bias Bounty Challenge. https:\/\/blog.twitter.com\/engineering\/en_us\/topics\/insights\/2021\/algorithmic-bias-bounty-challenge","key":"e_1_3_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1145\/3461702.3462523"},{"key":"e_1_3_2_1_10_1","volume-title":"Retiring Adult: New Datasets for Fair Machine Learning. Advances in Neural Information Processing Systems 34","author":"Ding Frances","year":"2021","unstructured":"Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021. Retiring Adult: New Datasets for Fair Machine Learning. Advances in Neural Information Processing Systems 34 (2021)."},{"unstructured":"Alex Franklin David Gagnon Maggie Meg Benner Natalie Rambis Perpetual Baffour Phil Culliton Scott Crossley and Ulrich Boser. 2023. Predict Student Performance from Game Play. https:\/\/kaggle.com\/competitions\/predict-student-performance-from-game-play","key":"e_1_3_2_1_11_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/3531146.3533172"},{"unstructured":"Addison Howard Archit Agarwal Ashley Chow Rantig Kellen\u00a0J Gracey Robert\u00a0JC Brown and Sohier Dane. 2022. GoDaddy - Microbusiness Density Forecasting. https:\/\/kaggle.com\/competitions\/godaddy-microbusiness-density-forecasting","key":"e_1_3_2_1_13_1"},{"unstructured":"Addison Howard Jevster Katherine Gustilo Katy Borner Ryan Holbrook and Yashvardhan Jain. 2023. HuBMAP - Hacking the Human Vasculature. https:\/\/kaggle.com\/competitions\/hubmap-hacking-the-human-vasculature","key":"e_1_3_2_1_14_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1145\/2382577.2382579"},{"key":"e_1_3_2_1_16_1","volume-title":"Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets. arXiv preprint arXiv:1908.04017","author":"Kowald Dominik","year":"2019","unstructured":"Dominik Kowald, Matthias Traub, Dieter Theiler, Heimo Gursch, Emanuel Lacic, Stefanie Lindstaedt, Roman Kern, and Elisabeth Lex. 2019. Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets. arXiv preprint arXiv:1908.04017 (2019)."},{"unstructured":"Alex Lourenco Brent Seales Christy Chapman Daniel Havir Ian\u00a0Janicki Janicki JP Posma Nat Friedman Ryan Holbrook Seth P. Stephen Parsons and Will Cukierski. 2023. Vesuvius Challenge - Ink Detection. https:\/\/kaggle.com\/competitions\/vesuvius-challenge-ink-detection","key":"e_1_3_2_1_17_1"},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. PMLR, 6755\u20136764","author":"Martinez Natalia","year":"2020","unstructured":"Natalia Martinez, Martin Bertran, and Guillermo Sapiro. 2020. Minimax pareto fairness: A multi objective perspective. In International Conference on Machine Learning. PMLR, 6755\u20136764."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1109\/IJCNN.2011.6033446"},{"unstructured":"Joe Ng Carl Elkin Aaron Sarna Walter Reade and Maggie Demkin. 2023. Google Research - Identify Contrails to Reduce Global Warming. https:\/\/kaggle.com\/competitions\/google-research-identify-contrails-reduce-global-warming","key":"e_1_3_2_1_20_1"},{"unstructured":"Amit Elazari\u00a0Bar On. 2018. We Need Bug Bounties for Bad Algorithms. https:\/\/www.vice.com\/en\/article\/8xkyj3\/we-need-bug-bounties-for-bad-algorithms","key":"e_1_3_2_1_21_1"},{"key":"e_1_3_2_1_22_1","volume-title":"A meta-analysis of overfitting in machine learning. Advances in Neural Information Processing Systems 32","author":"Roelofs Rebecca","year":"2019","unstructured":"Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, and Ludwig Schmidt. 2019. A meta-analysis of overfitting in machine learning. Advances in Neural Information Processing Systems 32 (2019)."},{"unstructured":"[23] Austin\u00a0Carson Sven\u00a0Cattell Rumman\u00a0Chowdhury. 2023. https:\/\/aivillage.org\/generative%20red%20team\/generative-red-team\/","key":"e_1_3_2_1_23_1"},{"key":"e_1_3_2_1_24_1","volume-title":"International Conference on Machine Learning. PMLR, 21633\u201321657","author":"Tosh J","year":"2022","unstructured":"Christopher\u00a0J Tosh and Daniel Hsu. 2022. Simple and near-optimal algorithms for hidden stratification and multi-group learning. In International Conference on Machine Learning. PMLR, 21633\u201321657."},{"unstructured":"Bojan Tunguz Dieter Karnika Kapoor Parul Pandey Paul Mooney Phil Culliton Rob Mulla Sanyam Bhutani and Will Cukierski. 2023. 2023 Kaggle AI Report. https:\/\/kaggle.com\/competitions\/2023-kaggle-ai-report","key":"e_1_3_2_1_25_1"}],"event":{"acronym":"FAccT '24","name":"FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency","location":"Rio de Janeiro Brazil"},"container-title":["The 2024 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658923","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630106.3658923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:30:29Z","timestamp":1755883829000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":25,"alternative-id":["10.1145\/3630106.3658923","10.1145\/3630106"],"URL":"https:\/\/doi.org\/10.1145\/3630106.3658923","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}