{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T16:15:29Z","timestamp":1780503329089,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":120,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T00:00:00Z","timestamp":1750636800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Flemish Government","award":["Onderzoeksprogramma Artifici\u00eble Intelligentie (AI) Vlaanderen"],"award-info":[{"award-number":["Onderzoeksprogramma Artifici\u00eble Intelligentie (AI) Vlaanderen"]}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["V437824N, G0F9816N, 3G042220, G073924N"],"award-info":[{"award-number":["V437824N, G0F9816N, 3G042220, G073924N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["VIGILIA, 101142229"],"award-info":[{"award-number":["VIGILIA, 101142229"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CAREER-1845852, FAI-2040880"],"award-info":[{"award-number":["CAREER-1845852, FAI-2040880"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017567","name":"Apple","doi-asserted-by":"publisher","award":["Scholars in AI\/ML Fellowship"],"award-info":[{"award-number":["Scholars in AI\/ML Fellowship"]}],"id":[{"id":"10.13039\/100017567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000269","name":"Economic and Social Research Council","doi-asserted-by":"publisher","award":["Grand Union Doctoral Training Partnership"],"award-info":[{"award-number":["Grand Union Doctoral Training Partnership"]}],"id":[{"id":"10.13039\/501100000269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,23]]},"DOI":"10.1145\/3715275.3732194","type":"proceedings-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T17:03:13Z","timestamp":1750698193000},"page":"3046-3074","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["AI Alignment at Your Discretion"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5434-2386","authenticated-orcid":false,"given":"Maarten","family":"Buyl","sequence":"first","affiliation":[{"name":"Ghent University, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7463-7915","authenticated-orcid":false,"given":"Hadi","family":"Khalaf","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2079-797X","authenticated-orcid":false,"given":"Claudio","family":"Mayrink Verdun","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0129-1420","authenticated-orcid":false,"given":"Lucas","family":"Monteiro Paes","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1396-2957","authenticated-orcid":false,"given":"Caio Cesar","family":"Vieira Machado","sequence":"additional","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom; Harvard University, Cambridge, USA and University of Sao Paulo, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7493-1428","authenticated-orcid":false,"given":"Flavio","family":"du Pin Calmon","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Gilad Abiri. 2024. Public Constitutional AI. Forthcoming in Georgia Law Review Volume 59 (2024).","DOI":"10.2139\/ssrn.4874670"},{"key":"e_1_3_3_2_3_2","unstructured":"Anthropic. 2024. Claude\u2019s Constitution. https:\/\/www.anthropic.com\/news\/claudes-constitution. Accessed: 2025-01-03."},{"key":"e_1_3_3_2_4_2","unstructured":"Anthropic. 2024. Model Card Addendum: Claude 3.5 Haiku and Upgraded Claude 3.5 Sonnet. https:\/\/www.anthropic.com\/model-cards\/claude-3.5."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Mohammad Atari Mona\u00a0J Xue Peter\u00a0S Park Dami\u00e1n Blasi and Joseph Henrich. 2023. Which humans? PsyArXiv (2023).","DOI":"10.31234\/osf.io\/5b26t"},{"key":"e_1_3_3_2_6_2","unstructured":"Yuntao Bai Andy Jones Kamal Ndousse Amanda Askell Anna Chen Nova DasSarma Dawn Drain Stanislav Fort Deep Ganguli Tom Henighan et\u00a0al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2204.05862 (2022)."},{"key":"e_1_3_3_2_7_2","unstructured":"Yuntao Bai Saurav Kadavath Sandipan Kundu Amanda Askell Jackson Kernion Andy Jones Anna Chen Anna Goldie Azalia Mirhoseini Cameron McKinnon et\u00a0al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2212.08073 (2022)."},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctt211qxt8"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1515\/9781400827046"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Nicholas Barrow. 2024. Anthropomorphism and AI hype. AI and Ethics (2024) 1\u20135.","DOI":"10.1007\/s43681-024-00454-1"},{"key":"e_1_3_3_2_11_2","first-page":"231","volume-title":"Reading Political Philosophy","author":"Berlin Isaiah","year":"2014","unstructured":"Isaiah Berlin. 2014. \u2018Two Concepts of Liberty\u2019. In Reading Political Philosophy. Routledge, 231\u2013237."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372860"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.81"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Ralph\u00a0Allan Bradley and Milton\u00a0E Terry. 1952. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika 39 3\/4 (1952) 324\u2013345.","DOI":"10.1093\/biomet\/39.3-4.324"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Federico Cabitza Andrea Campagner and Valerio Basile. 2023. Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. Proceedings of the AAAI Conference on Artificial Intelligence 37 6 (June 2023) 6860\u20136868. 10.1609\/aaai.v37i6.25840","DOI":"10.1609\/aaai.v37i6.25840"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Nicholas Caputo. 2024. Alignment as Jurisprudence. Yale Journal of Law and Technology (forthcoming) (2024). https:\/\/ssrn.com\/abstract=4800894","DOI":"10.2139\/ssrn.4800894"},{"key":"e_1_3_3_2_17_2","unstructured":"Stephen Casper Xander Davies Claudia Shi Thomas Krendl\u00a0Gilbert J\u00e9r\u00e9my Scheurer Javier Rando\u00a0Ramirez Rachel Freedman Tomasz Korbak David Lindner Pedro Freire et\u00a0al. 2023. Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_3_2_18_2","first-page":"6596","volume-title":"International Conference on Machine Learning","author":"Chen Xinyun","year":"2024","unstructured":"Xinyun Chen, Ryan\u00a0Andrew Chi, Xuezhi Wang, and Denny Zhou. 2024. Premise Order Matters in Reasoning with Large Language Models. In International Conference on Machine Learning. PMLR, 6596\u20136620."},{"key":"e_1_3_3_2_19_2","unstructured":"Wei-Lin Chiang Lianmin Zheng Ying Sheng Anastasios\u00a0Nikolas Angelopoulos Tianle Li Dacheng Li Hao Zhang Banghua Zhu Michael Jordan Joseph\u00a0E Gonzalez et\u00a0al. 2024. Chatbot arena: An open platform for evaluating llms by human preference. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.04132 (2024)."},{"key":"e_1_3_3_2_20_2","unstructured":"Paul\u00a0F Christiano Jan Leike Tom Brown Miljan Martic Shane Legg and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","unstructured":"Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20 1 (1960) 37\u201346. 10.1177\/001316446002000104 arXiv:10.1177\/001316446002000104","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","unstructured":"Jacob Cohen. 1968. Weighted Kappa: Nominal Scale Agreement Provision for Scaled Disagreement or Partial Credit. Psychological Bulletin 70 4 (1968) 213\u2013220. 10.1037\/h0026256","DOI":"10.1037\/h0026256"},{"key":"e_1_3_3_2_23_2","volume-title":"Forty-first International Conference on Machine Learning","author":"Conitzer Vincent","year":"2024","unstructured":"Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley\u00a0H Holliday, Bob\u00a0M Jacobs, Nathan Lambert, Milan Moss\u00e9, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, et\u00a0al. 2024. Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_3_2_24_2","volume-title":"Forty-first International Conference on Machine Learning","author":"Cui Ganqu","year":"2024","unstructured":"Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, et\u00a0al. 2024. ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3659021"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Roger\u00a0R Davidson. 1970. On extending the Bradley-Terry model to accommodate ties in paired comparison experiments. J. Amer. Statist. Assoc. 65 329 (1970) 317\u2013328.","DOI":"10.1080\/01621459.1970.10481082"},{"key":"e_1_3_3_2_27_2","unstructured":"Roger\u00a0R Davidson and Peter\u00a0H Farquhar. 1976. A bibliography on the method of paired comparisons. Biometrics (1976) 241\u2013252."},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594000"},{"key":"e_1_3_3_2_29_2","volume-title":"Discretionary Justice: A Preliminary Inquiry","author":"Davis Kenneth\u00a0Culp","year":"1969","unstructured":"Kenneth\u00a0Culp Davis. 1969. Discretionary Justice: A Preliminary Inquiry. Lousiana State University Press."},{"key":"e_1_3_3_2_30_2","unstructured":"DeepSeek-AI Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan Damai Dai Daya Guo Dejian Yang Deli Chen Dongjie Ji Erhang Li Fangyun Lin Fucong Dai Fuli Luo Guangbo Hao Guanting Chen Guowei Li H. Zhang Han Bao Hanwei Xu Haocheng Wang Haowei Zhang Honghui Ding Huajian Xin Huazuo Gao Hui Li Hui Qu J.\u00a0L. Cai Jian Liang Jianzhong Guo Jiaqi Ni Jiashi Li Jiawei Wang Jin Chen Jingchang Chen Jingyang Yuan Junjie Qiu Junlong Li Junxiao Song Kai Dong Kai Hu Kaige Gao Kang Guan Kexin Huang Kuai Yu Lean Wang Lecong Zhang Lei Xu Leyi Xia Liang Zhao Litong Wang Liyue Zhang Meng Li Miaojun Wang Mingchuan Zhang Minghua Zhang Minghui Tang Mingming Li Ning Tian Panpan Huang Peiyi Wang Peng Zhang Qiancheng Wang Qihao Zhu Qinyu Chen Qiushi Du R.\u00a0J. Chen R.\u00a0L. Jin Ruiqi Ge Ruisong Zhang Ruizhe Pan Runji Wang Runxin Xu Ruoyu Zhang Ruyi Chen S.\u00a0S. Li Shanghao Lu Shangyan Zhou Shanhuang Chen Shaoqing Wu Shengfeng Ye Shengfeng Ye Shirong Ma Shiyu Wang Shuang Zhou Shuiping Yu Shunfeng Zhou Shuting Pan T. Wang Tao Yun Tian Pei Tianyu Sun W.\u00a0L. Xiao Wangding Zeng Wanjia Zhao Wei An Wen Liu Wenfeng Liang Wenjun Gao Wenqin Yu Wentao Zhang X.\u00a0Q. Li Xiangyue Jin Xianzu Wang Xiao Bi Xiaodong Liu Xiaohan Wang Xiaojin Shen Xiaokang Chen Xiaokang Zhang Xiaosha Chen Xiaotao Nie Xiaowen Sun Xiaoxiang Wang Xin Cheng Xin Liu Xin Xie Xingchao Liu Xingkai Yu Xinnan Song Xinxia Shan Xinyi Zhou Xinyu Yang Xinyuan Li Xuecheng Su Xuheng Lin Y.\u00a0K. Li Y.\u00a0Q. Wang Y.\u00a0X. Wei Y.\u00a0X. Zhu Yang Zhang Yanhong Xu Yanhong Xu Yanping Huang Yao Li Yao Zhao Yaofeng Sun Yaohui Li Yaohui Wang Yi Yu Yi Zheng Yichao Zhang Yifan Shi Yiliang Xiong Ying He Ying Tang Yishi Piao Yisong Wang Yixuan Tan Yiyang Ma Yiyuan Liu Yongqiang Guo Yu Wu Yuan Ou Yuchen Zhu Yuduan Wang Yue Gong Yuheng Zou Yujia He Yukun Zha Yunfan Xiong Yunxian Ma Yuting Yan Yuxiang Luo Yuxiang You Yuxuan Liu Yuyang Zhou Z.\u00a0F. Wu Z.\u00a0Z. Ren Zehui Ren Zhangli Sha Zhe Fu Zhean Xu Zhen Huang Zhen Zhang Zhenda Xie Zhengyan Zhang Zhewen Hao Zhibin Gou Zhicheng Ma Zhigang Yan Zhihong Shao Zhipeng Xu Zhiyu Wu Zhongyu Zhang Zhuoshu Li Zihui Gu Zijia Zhu Zijun Liu Zilin Li Ziwei Xie Ziyang Song Ziyi Gao and Zizheng Pan. 2024. DeepSeek-V3 Technical Report. arxiv:https:\/\/arXiv.org\/abs\/2412.19437\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2412.19437"},{"key":"e_1_3_3_2_31_2","unstructured":"Hanze Dong Wei Xiong Bo Pang Haoxiang Wang Han Zhao Yingbo Zhou Nan Jiang Doyen Sahoo Caiming Xiong and Tong Zhang. 2024. RLHF Workflow: From Reward Modeling to Online RLHF. Transactions on Machine Learning Research (2024). https:\/\/openreview.net\/forum?id=a13aYUU9eU"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Yi Dong Zhilin Wang Makesh\u00a0Narsimhan Sreedhar Xianchao Wu and Oleksii Kuchaiev. 2023. Steerlm: Attribute conditioned sft as an (user-steerable) alternative to rlhf. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.05344 (2023).","DOI":"10.18653\/v1\/2023.findings-emnlp.754"},{"key":"e_1_3_3_2_33_2","unstructured":"Yann Dubois Chen\u00a0Xuechen Li Rohan Taori Tianyi Zhang Ishaan Gulrajani Jimmy Ba Carlos Guestrin Percy\u00a0S Liang and Tatsunori\u00a0B Hashimoto. 2024. Alpacafarm: A simulation framework for methods that learn from human feedback. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_3_2_34_2","volume-title":"Law\u2019s empire","author":"Dworkin Ronald","year":"1986","unstructured":"Ronald Dworkin. 1986. Law\u2019s empire. Harvard University Press."},{"key":"e_1_3_3_2_35_2","volume-title":"Taking rights seriously","author":"Dworkin Ronald","year":"2013","unstructured":"Ronald Dworkin. 2013. Taking rights seriously. A&C Black."},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534647"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450091"},{"key":"e_1_3_3_2_38_2","unstructured":"Arpad\u00a0Emrick Elo. 1978. The Rating of Chessplayers: Past and Present. Batsford Chess Books (1978)."},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","unstructured":"OpenAI et al.2024. GPT-4 Technical Report. 10.48550\/arXiv.2303.08774 arxiv:https:\/\/arXiv.org\/abs\/2303.08774\u00a0[cs]","DOI":"10.48550\/arXiv.2303.08774"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i18.30020"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i5.25739"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.240"},{"key":"e_1_3_3_2_43_2","unstructured":"Arduin Findeis Timo Kaufmann Eyke H\u00fcllermeier Samuel Albanie and Robert Mullins. 2024. Inverse Constitutional AI: Compressing Preferences into Principles. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.06560 (2024)."},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","unstructured":"Joseph Fleiss. 1971. Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin 76 (11 1971) 378\u2013. 10.1037\/h0031619","DOI":"10.1037\/h0031619"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Isabel\u00a0O Gallegos Ryan\u00a0A Rossi Joe Barrow Md\u00a0Mehrab Tanjim Sungchul Kim Franck Dernoncourt Tong Yu Ruiyi Zhang and Nesreen\u00a0K Ahmed. 2024. Bias and fairness in large language models: A survey. Computational Linguistics 50 3 (2024) 1097\u20131179.","DOI":"10.1162\/coli_a_00524"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372862"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","unstructured":"Fabrizio Gilardi Meysam Alizadeh and Ma\u00ebl Kubli. 2023. ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences 120 30 (July 2023). 10.1073\/pnas.2305016120","DOI":"10.1073\/pnas.2305016120"},{"key":"e_1_3_3_2_48_2","unstructured":"Melody\u00a0Y Guan Manas Joglekar Eric Wallace Saachi Jain Boaz Barak Alec Helyar Rachel Dias Andrea Vallone Hongyu Ren Jason Wei et\u00a0al. 2024. Deliberative alignment: Reasoning enables safer language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.16339 (2024)."},{"key":"e_1_3_3_2_49_2","unstructured":"Ian Hamilton Nick Tawn and David Firth. 2023. The many routes to the ubiquitous Bradley-Terry model. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2312.13619 (2023)."},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1093\/he\/9780199644704.001.0001"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"publisher","unstructured":"Lars Hornuf and Daniel Vrankar. 2022. Hourly Wages in Crowdworking: A Meta-Analysis. Business \\(\\&\\) Information Systems Engineering 64 5 (Aug. 2022) 553\u2013573. 10.1007\/s12599-022-00769-5","DOI":"10.1007\/s12599-022-00769-5"},{"key":"e_1_3_3_2_52_2","unstructured":"Saffron Huang Esin Durmus Miles McCain Kunal Handa Alex Tamkin Jerry Hong Michael Stern Arushi Somani Xiuruo Zhang and Deep Ganguli. 2025. Values in the wild: Discovering and analyzing values in real-world language model interactions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2504.15236 (2025)."},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658979"},{"key":"e_1_3_3_2_54_2","unstructured":"Tzu-Kuo Huang Ruby\u00a0C Weng Chih-Jen Lin and Greg Ridgeway. 2006. Generalized Bradley-Terry Models and Multi-Class Probability Estimates. Journal of Machine Learning Research 7 1 (2006)."},{"key":"e_1_3_3_2_55_2","unstructured":"Yue Huang Lichao Sun Haoran Wang Siyuan Wu Qihui Zhang Yuan Li Chujie Gao Yixin Huang Wenhan Lyu Yixuan Zhang et\u00a0al. 2024. Trustllm: Trustworthiness in large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2401.05561 (2024)."},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445918"},{"key":"e_1_3_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3659040"},{"key":"e_1_3_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658899"},{"key":"e_1_3_3_2_59_2","unstructured":"Jiaming Ji Donghai Hong Borong Zhang Boyuan Chen Josef Dai Boren Zheng Tianyi Qiu Boxun Li and Yaodong Yang. 2024. Pku-saferlhf: Towards multi-level safety alignment for llms with human preference. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.15513 (2024)."},{"key":"e_1_3_3_2_60_2","unstructured":"Jiaming Ji Tianyi Qiu Boyuan Chen Borong Zhang Hantao Lou Kaile Wang Yawen Duan Zhonghao He Jiayi Zhou Zhaowei Zhang et\u00a0al. 2023. Ai alignment: A comprehensive survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.19852 (2023)."},{"key":"e_1_3_3_2_61_2","unstructured":"Albert Jiang Alexandre Sablayrolles Arthur Mensch Blanche Savary Chris Bamford Devendra\u00a0Singh Chaplot Diego de\u00a0las Casas Emma\u00a0Bou Hanna Florian Bressand Gianna Lengyel Guillaume Bour Guillaume Lample L\u00e9lio\u00a0Renard Lavaud Louis Ternon Lucile Saulnier Marie-Anne Lachaux Pierre Stock Teven\u00a0Le Scao Th\u00e9ophile Gervet Thibaut Lavril Thomas Wang Timoth\u00e9e Lacroix and William\u00a0El Sayed. 2025. Mistral-7B-Instruct-v0.2. https:\/\/huggingface.co\/mistralai\/Mistral-7B-Instruct-v0.2."},{"key":"e_1_3_3_2_62_2","unstructured":"Albert\u00a0Q. Jiang Alexandre Sablayrolles Arthur Mensch Chris Bamford Devendra\u00a0Singh Chaplot Diego de\u00a0las Casas Florian Bressand Gianna Lengyel Guillaume Lample Lucile Saulnier L\u00e9lio Renard\u00a0Lavaud Marie-Anne Lachaux Pierre Stock Teven Le\u00a0Scao Thibaut Lavril Thomas Wang Timoth\u00e9e Lacroix and William El\u00a0Sayed. 2023. Mistral 7B. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.06825 (2023)."},{"key":"e_1_3_3_2_63_2","volume-title":"The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track","author":"Kirk Hannah\u00a0Rose","year":"2024","unstructured":"Hannah\u00a0Rose Kirk, Alexander Whitefield, Paul R\u00f6ttger, Andrew\u00a0Michael Bean, Katerina Margatina, Rafael Mosquera, Juan\u00a0Manuel Ciro, Max Bartolo, Adina Williams, He He, et\u00a0al. 2024. The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_3_3_2_64_2","volume-title":"Pluralistic Alignment Workshop at NeurIPS 2024","author":"Klassen Toryn\u00a0Q","year":"2024","unstructured":"Toryn\u00a0Q Klassen, Parand\u00a0A Alamdari, and Sheila\u00a0A McIlraith. 2024. Pluralistic Alignment Over Time. In Pluralistic Alignment Workshop at NeurIPS 2024."},{"key":"e_1_3_3_2_65_2","unstructured":"Oliver Klingefjord Ryan Lowe and Joe Edelman. 2024. What are human values and how do we align AI to them? arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.10636 (2024)."},{"key":"e_1_3_3_2_66_2","doi-asserted-by":"crossref","unstructured":"Terry\u00a0K Koo and Mae\u00a0Y Li. 2016. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine 15 2 (2016) 155\u2013163.","DOI":"10.1016\/j.jcm.2016.02.012"},{"key":"e_1_3_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772749"},{"key":"e_1_3_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199575169.001.0001"},{"key":"e_1_3_3_2_69_2","unstructured":"Harrison Lee Samrat Phatale Hassan Mansoor Kellie\u00a0Ren Lu Thomas Mesnard Johan Ferret Colton Bishop Ethan Hall Victor Carbune and Abhinav Rastogi. 2023. Rlaif: Scaling reinforcement learning from human feedback with ai feedback. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2309.00267 (2023)."},{"key":"e_1_3_3_2_70_2","unstructured":"Junlong Li Fan Zhou Shichao Sun Yikai Zhang Hai Zhao and Pengfei Liu. 2024. Dissecting Human and LLM Preferences. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.11296 (2024)."},{"key":"e_1_3_3_2_71_2","unstructured":"Minzhi Li Zhengyuan Liu Shumin Deng Shafiq Joty Nancy\u00a0F Chen and Min-Yen Kan. 2024. Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2405.15329 (2024)."},{"key":"e_1_3_3_2_72_2","unstructured":"Tianle Li Wei-Lin Chiang Evan Frick Lisa Dunlap Tianhao Wu Banghua Zhu Joseph\u00a0E Gonzalez and Ion Stoica. 2024. From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.11939 (2024)."},{"key":"e_1_3_3_2_73_2","unstructured":"Xiaomin Li Mingye Gao Zhiwei Zhang Jingxuan Fan and Weiyu Li. 2025. Data-adaptive Safety Rules for Training Reward Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2501.15453 (2025)."},{"key":"e_1_3_3_2_74_2","unstructured":"Yinhong Liu Han Zhou Zhijiang Guo Ehsan Shareghi Ivan Vulic Anna Korhonen and Nigel Collier. 2024. Aligning with human judgement: The role of pairwise preference in large language model evaluators. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.16950 (2024)."},{"key":"e_1_3_3_2_75_2","volume-title":"Individual choice behavior","author":"Luce R\u00a0Duncan","year":"1959","unstructured":"R\u00a0Duncan Luce. 1959. Individual choice behavior. Vol.\u00a04. Wiley New York."},{"key":"e_1_3_3_2_76_2","unstructured":"Chenyang Lyu Minghao Wu and Alham\u00a0Fikri Aji. 2024. Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2402.13887\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2402.13887"},{"key":"e_1_3_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203123638"},{"key":"e_1_3_3_2_78_2","unstructured":"Gemma Team\u00a0Thomas Mesnard Cassidy Hardin Robert Dadashi Surya Bhupatiraju Shreya Pathak L. Sifre Morgane Rivi\u00e8re Mihir Kale J\u00a0Christopher Love Pouya\u00a0Dehghani Tafti L\u2019eonard Hussenot Aakanksha Chowdhery Adam Roberts Aditya Barua Alex Botev Alex Castro-Ros Ambrose Slone Am\u2019elie H\u2019eliou Andrea Tacchetti Anna Bulanova Antonia Paterson Beth Tsai Bobak Shahriari Charline\u00a0Le Lan Christopher\u00a0A. Choquette-Choo Cl\u00e9 ment Crepy Daniel Cer Daphne Ippolito David Reid Elena Buchatskaya Eric Ni Eric Noland Geng Yan George Tucker George-Christian Muraru Grig ory Rozhdestvenskiy Henryk Michalewski Ian Tenney Ivan Grishchenko Jacob Austin James Keeling Jane Labanowski Jean-Baptiste Lespiau Jeff Stanway Jenny Brennan Jeremy Chen Johan Ferret Justin Chiu Justin Mao-Jones Katherine Lee Kathy Yu Katie Millican Lars\u00a0Lowe Sjoesund Lisa Lee Lucas Dixon Machel Reid Maciej Miku\u0142a Mateo Wirth Michael Sharman Nikolai Chinaev Nithum Thain Olivier Bachem Oscar Chang Oscar Wahltinez Paige Bailey Paul Michel Petko Yotov Pier\u00a0Giuseppe Sessa Rahma Chaabouni Ramona Comanescu Reena Jana Rohan Anil Ross McIlroy Ruibo Liu Ryan Mullins Samuel\u00a0L Smith Sebastian Borgeaud Sertan Girgin Sholto Douglas Shree Pandya Siamak Shakeri Soham De Ted Klimenko Tom Hennigan Vladimir Feinberg Wojciech Stokowiec Yu hui Chen Zafarali Ahmed Zhitao Gong Tris Warkentin Ludovic Peran Minh Giang Cl\u00e9ment Farabet Oriol Vinyals Jeffrey Dean Koray Kavukcuoglu Demis Hassabis Zoubin Ghahramani Douglas Eck Joelle Barral Fernando Pereira Eli Collins Armand Joulin Noah Fiedel Evan Senter Alek Andreev and Kathleen Kenealy. 2024. Gemma: Open Models Based on Gemini Research and Technology. ArXiv abs\/2403.08295 (2024). https:\/\/api.semanticscholar.org\/CorpusID:268379206"},{"key":"e_1_3_3_2_79_2","doi-asserted-by":"publisher","unstructured":"Aida Mostafazadeh\u00a0Davani Mark D\u00edaz and Vinodkumar Prabhakaran. 2022. Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations. Transactions of the Association for Computational Linguistics 10 (2022) 92\u2013110. 10.1162\/tacl_a_00449","DOI":"10.1162\/tacl_a_00449"},{"key":"e_1_3_3_2_80_2","volume-title":"ICML 2024 Next Generation of AI Safety Workshop","author":"Mu Tong","year":"2024","unstructured":"Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian\u00a0D Kivlichan, Molly Lin, Alex Beutel, John Schulman, and Lilian Weng. 2024. Rule Based Rewards for Fine-Grained LLM Safety. In ICML 2024 Next Generation of AI Safety Workshop. https:\/\/openreview.net\/forum?id=Qkao05dRAe"},{"key":"e_1_3_3_2_81_2","unstructured":"Randall Munroe. 2015. xkcd #1613: \u201cThree Laws of Robotics\u201d. https:\/\/xkcd.com\/1613\/. Accessed: 2025-01-18."},{"key":"e_1_3_3_2_82_2","unstructured":"John\u00a0J. Nay. 2024. Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans. SSRN Working Paper (2024)."},{"key":"e_1_3_3_2_83_2","unstructured":"Ike Obi Rohan Pant Srishti\u00a0Shekhar Agrawal Maham Ghazanfar and Aaron Basiletti. 2024. Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.11937 (2024)."},{"key":"e_1_3_3_2_84_2","unstructured":"Office of the United Nations High Commissioner for Human Rights (OHCHR). [n. d.]. International Covenant on Civil and Political Rights. https:\/\/www.ohchr.org\/en\/instruments-mechanisms\/instruments\/international-covenant-civil-and-political-rights. Accessed: 2025-01-22."},{"key":"e_1_3_3_2_85_2","unstructured":"OpenAI. 2024. GPT-4o System Card. arXiv:arXiv:2410.21276"},{"key":"e_1_3_3_2_86_2","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et\u00a0al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022) 27730\u201327744."},{"key":"e_1_3_3_2_87_2","unstructured":"Arjun Panickssery Samuel\u00a0R Bowman and Shi Feng. 2024. Llm evaluators recognize and favor their own generations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.13076 (2024)."},{"key":"e_1_3_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.130"},{"key":"e_1_3_3_2_89_2","doi-asserted-by":"crossref","unstructured":"Karl Pearson. 1896. VII. Mathematical contributions to the theory of evolution.\u2014III. Regression heredity and panmixia. Philosophical Transactions of the Royal Society of London. Series A containing papers of a mathematical or physical character187 (1896) 253\u2013318.","DOI":"10.1098\/rsta.1896.0007"},{"key":"e_1_3_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.225"},{"key":"e_1_3_3_2_91_2","unstructured":"Silviu Pitis Ziang Xiao Nicolas\u00a0Le Roux and Alessandro Sordoni. 2024. Improving context-aware preference modeling for language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.14916 (2024)."},{"key":"e_1_3_3_2_92_2","doi-asserted-by":"crossref","unstructured":"Robin\u00a0L Plackett. 1975. The analysis of permutations. Journal of the Royal Statistical Society Series C: Applied Statistics 24 2 (1975) 193\u2013202.","DOI":"10.2307\/2346567"},{"key":"e_1_3_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.731"},{"key":"e_1_3_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.law-1.14"},{"key":"e_1_3_3_2_95_2","unstructured":"Ravi Raju Swayambhoo Jain Bo Li Jonathan Li and Urmish Thakker. 2024. Constructing domain-specific evaluation sets for llm-as-a-judge. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2408.08808 (2024)."},{"key":"e_1_3_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.892"},{"key":"e_1_3_3_2_97_2","volume-title":"The authority of law: essays on law and morality","author":"Raz Joseph","year":"2009","unstructured":"Joseph Raz. 2009. The authority of law: essays on law and morality. Oxford University Press."},{"key":"e_1_3_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.275"},{"key":"e_1_3_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.178"},{"key":"e_1_3_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.431"},{"key":"e_1_3_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1093\/law\/9780199594061.001.0001"},{"key":"e_1_3_3_2_102_2","unstructured":"Nino Scherrer Claudia Shi Amir Feder and David Blei. 2023. Evaluating the Moral Beliefs Encoded in LLMs. Advances in Neural Information Processing Systems 36 (Dec. 2023) 51778\u201351809."},{"key":"e_1_3_3_2_103_2","unstructured":"Hua Shen Tiffany Knearem Reshmi Ghosh Kenan Alkiek Kundan Krishna Yachuan Liu Ziqiao Ma Savvas Petridis Yi-Hao Peng Li Qiwei et\u00a0al. 2024. Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications Framework and Future Directions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.09264 (2024)."},{"key":"e_1_3_3_2_104_2","doi-asserted-by":"publisher","unstructured":"Taylor Sorensen Liwei Jiang Jena\u00a0D. Hwang Sydney Levine Valentina Pyatkin Peter West Nouha Dziri Ximing Lu Kavel Rao Chandra Bhagavatula Maarten Sap John Tasioulas and Yejin Choi. 2024. Value Kaleidoscope: Engaging AI with Pluralistic Human Values Rights and Duties. Proceedings of the AAAI Conference on Artificial Intelligence 38 18 (March 2024) 19937\u201319947. 10.1609\/aaai.v38i18.29970","DOI":"10.1609\/aaai.v38i18.29970"},{"key":"e_1_3_3_2_105_2","unstructured":"Taylor Sorensen Jared Moore Jillian Fisher Mitchell Gordon Niloofar Mireshghallah Christopher\u00a0Michael Rytting Andre Ye Liwei Jiang Ximing Lu Nouha Dziri et\u00a0al. 2024. A roadmap to pluralistic alignment. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.05070 (2024)."},{"key":"e_1_3_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1037\/11491-005"},{"key":"e_1_3_3_2_107_2","unstructured":"Nisan Stiennon Long Ouyang Jeffrey Wu Daniel Ziegler Ryan Lowe Chelsea Voss Alec Radford Dario Amodei and Paul\u00a0F Christiano. 2020. Learning to summarize with human feedback. Advances in Neural Information Processing Systems 33 (2020) 3008\u20133021."},{"key":"e_1_3_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.330"},{"key":"e_1_3_3_2_109_2","doi-asserted-by":"crossref","unstructured":"Amos Tversky and Itamar Simonson. 1993. Context-Dependent Preferences. Manage. Sci. 39 10 (Oct. 1993) 1179\u20131189.","DOI":"10.1287\/mnsc.39.10.1179"},{"key":"e_1_3_3_2_110_2","unstructured":"Leandro von Werra Younes Belkada Lewis Tunstall Edward Beeching Tristan Thrush Nathan Lambert Shengyi Huang Kashif Rasul and Quentin Gallou\u00e9dec. 2020. TRL: Transformer Reinforcement Learning. https:\/\/github.com\/huggingface\/trl. GitHub repository (2020)."},{"key":"e_1_3_3_2_111_2","unstructured":"Jiashuo Wang Haozhao Wang Shichao Sun and Wenjie Li. 2024. Aligning language models with human preferences via a bayesian approach. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_3_2_112_2","unstructured":"Peiyi Wang Lei Li Liang Chen Zefan Cai Dawei Zhu Binghuai Lin Yunbo Cao Qi Liu Tianyu Liu and Zhifang Sui. 2023. Large language models are not fair evaluators. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2305.17926 (2023)."},{"key":"e_1_3_3_2_113_2","unstructured":"Hui Wei Shenghua He Tian Xia Andy Wong Jingyang Lin and Mei Han. 2024. Systematic evaluation of llm-as-a-judge in llm alignment tasks: Explainable metrics and diverse prompt templates. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2408.13006 (2024)."},{"key":"e_1_3_3_2_114_2","doi-asserted-by":"crossref","unstructured":"Christian Wirth Riad Akrour Gerhard Neumann and Johannes F\u00fcrnkranz. 2017. A survey of preference-based reinforcement learning methods. Journal of Machine Learning Research 18 136 (2017) 1\u201346.","DOI":"10.1609\/aaai.v30i1.10269"},{"key":"e_1_3_3_2_115_2","unstructured":"Minghao Wu and Alham\u00a0Fikri Aji. 2023. Style over substance: Evaluation biases for large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2307.03025 (2023)."},{"key":"e_1_3_3_2_116_2","doi-asserted-by":"crossref","unstructured":"Eunice Yiu Eliza Kosoy and Alison Gopnik. 2024. Transmission versus truth imitation versus innovation: What children can do that large language and language-and-vision models cannot (yet). Perspectives on Psychological Science 19 5 (2024) 874\u2013883.","DOI":"10.1177\/17456916231201401"},{"key":"e_1_3_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.445"},{"key":"e_1_3_3_2_118_2","unstructured":"Michael\u00a0JQ Zhang Zhilin Wang Jena\u00a0D Hwang Yi Dong Olivier Delalleau Yejin Choi Eunsol Choi Xiang Ren and Valentina Pyatkin. 2024. Diverging Preferences: When do Annotators Disagree and do Models Know? arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.14632 (2024)."},{"key":"e_1_3_3_2_119_2","unstructured":"Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu Yonghao Zhuang Zi Lin Zhuohan Li Dacheng Li Eric Xing et\u00a0al. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36 (2023) 46595\u201346623."},{"key":"e_1_3_3_2_120_2","unstructured":"Daniel\u00a0M Ziegler Nisan Stiennon Jeffrey Wu Tom\u00a0B Brown Alec Radford Dario Amodei Paul Christiano and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1909.08593 (2019)."},{"key":"e_1_3_3_2_121_2","doi-asserted-by":"crossref","unstructured":"Caleb Ziems William Held Omar Shaikh Jiaao Chen Zhehao Zhang and Diyi Yang. 2024. Can large language models transform computational social science? Computational Linguistics 50 1 (2024) 237\u2013291.","DOI":"10.1162\/coli_a_00502"}],"event":{"name":"FAccT '25: The 2025 ACM Conference on Fairness, Accountability, and Transparency","location":"Athens Greece","acronym":"FAccT '25"},"container-title":["Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3715275.3732194","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3715275.3732194","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T11:27:15Z","timestamp":1750764435000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715275.3732194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,23]]},"references-count":120,"alternative-id":["10.1145\/3715275.3732194","10.1145\/3715275"],"URL":"https:\/\/doi.org\/10.1145\/3715275.3732194","relation":{},"subject":[],"published":{"date-parts":[[2025,6,23]]},"assertion":[{"value":"2025-06-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}