{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:14Z","timestamp":1786120814256,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Science Foundation","award":["2112471"],"award-info":[{"award-number":["2112471"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3818648","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:37Z","timestamp":1786036537000},"page":"13232-13238","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Auditing AI Systems in the Wild"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8713-2566","authenticated-orcid":false,"given":"Aditya T.","family":"Vadlamani","sequence":"first","affiliation":[{"name":"Ohio State University, Columbus, Ohio, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7982-7251","authenticated-orcid":false,"given":"Anutam","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6062-6449","authenticated-orcid":false,"given":"Srinivasan","family":"Parthasarathy","sequence":"additional","affiliation":[{"name":"Ohio State University, Columbus, Ohio, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Recommender Systems (1 ed.)","author":"Aggarwal Charu C","unstructured":"Charu C Aggarwal. 2016. Recommender Systems (1 ed.). Springer International Publishing, Cham, Switzerland."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/iotm.001.2300135"},{"key":"e_1_3_2_1_3_1","unstructured":"Anastasios N. Angelopoulos Stephen Bates Clara Fannjiang Michael I. Jordan and Tijana Zrnic. 2023. Prediction-Powered Inference. arXiv:2301.09633 [stat.ML] https:\/\/arxiv.org\/abs\/2301.09633"},{"key":"e_1_3_2_1_4_1","volume-title":"Conformal Risk Control. In The Twelfth International Conference on Learning Representations.","author":"Angelopoulos Anastasios Nikolas","year":"2024","unstructured":"Anastasios Nikolas Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster. 2024. Conformal Risk Control. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","first-page":"254","article-title":"Machine bias. In Ethics of Data and Analytics","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics. Auerbach Publications, 254-264.","journal-title":"Auerbach Publications"},{"key":"e_1_3_2_1_6_1","volume-title":"System card: Claude opus 4 & claude sonnet 4. Claude-4 Model Card","author":"Anthropic AI","year":"2025","unstructured":"AI Anthropic. 2025. System card: Claude opus 4 & claude sonnet 4. Claude-4 Model Card (2025)."},{"key":"e_1_3_2_1_7_1","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2020. Invariant Risk Minimization. arXiv:1907.02893 [stat.ML] https:\/\/arxiv.org\/abs\/1907.02893"},{"key":"e_1_3_2_1_8_1","volume-title":"The Limits of Organization","author":"Arrow K.J.","year":"1906","unstructured":"K.J. Arrow. 1974. The Limits of Organization. Norton. 74190643 https:\/\/books.google.com\/books?id=_JHZAAAAMAAJ"},{"key":"e_1_3_2_1_9_1","volume-title":"Fairness and machine learning: Limitations and opportunities","author":"Barocas Solon","unstructured":"Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and machine learning: Limitations and opportunities. MIT press."},{"key":"e_1_3_2_1_10_1","volume-title":"Yonghoon Lee, Edgar Dobriban, and Yaniv Romano.","author":"Bashari Meshi","year":"2025","unstructured":"Meshi Bashari, Roy Maor Lotan, Yonghoon Lee, Edgar Dobriban, and Yaniv Romano. 2025. Synthetic-Powered Predictive Inference. arXiv:2505.13432 [cs.LG] https:\/\/arxiv.org\/abs\/2505.13432"},{"key":"e_1_3_2_1_11_1","unstructured":"Yoshua Bengio Stephen Clare Carina Prunkl Maksym Andriushchenko Ben Bucknall Malcolm Murray Rishi Bommasani Stephen Casper Tom Davidson Raymond Douglas et al. 2026. International AI Safety Report 2026. arXiv preprint arXiv:2602.21012 (2026)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-024-02136-1"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240370"},{"key":"e_1_3_2_1_14_1","first-page":"307","volume-title":"33rd USENIX Security Symposium (USENIX Security 24)","author":"Chang Hongyan","year":"2024","unstructured":"Hongyan Chang, Brandon Edwards, Anindya S Paul, and Reza Shokri. 2024. Efficient privacy auditing in federated learning. In 33rd USENIX Security Symposium (USENIX Security 24). 307-323."},{"key":"e_1_3_2_1_15_1","volume-title":"Laurie Lovett Novak, Shilo Anders, and Bradley Malin.","author":"Chen You","year":"2023","unstructured":"You Chen, Ellen Wright Clayton, Laurie Lovett Novak, Shilo Anders, and Bradley Malin. 2023. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res, V25 (2023), e43251."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295184"},{"key":"e_1_3_2_1_17_1","volume-title":"Conformal Prediction Sets Can Cause Disparate Impact. In The Thirteenth International Conference on Learning Representations.","author":"Cresswell Jesse C","year":"2025","unstructured":"Jesse C Cresswell, Bhargava Kumar, Yi Sui, and Mouloud Belbahri. 2025. Conformal Prediction Sets Can Cause Disparate Impact. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3617694.3623261"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CTS.2013.6567203"},{"key":"e_1_3_2_1_20_1","volume-title":"COMPAS risk scales: Demonstrating accuracy equity and predictive parity","author":"Dieterich William","year":"2016","unstructured":"William Dieterich, Christina Mendoza, and Tim Brennan. 2016. COMPAS risk scales: Demonstrating accuracy equity and predictive parity. Northpointe Inc, V7, 4 (2016)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Matthew F Dixon Igor Halperin Paul Bilokon et al. 2020. Machine learning in finance. Vol. V1170. Springer.","DOI":"10.1007\/978-3-030-41068-1"},{"key":"e_1_3_2_1_22_1","volume-title":"Uniform guidelines on employee selection procedures. (March","author":"The U.S. EEOC.","year":"1979","unstructured":"The U.S. EEOC. 1979. Uniform guidelines on employee selection procedures. (March 1979)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00370-7"},{"key":"e_1_3_2_1_24_1","volume-title":"Embodied ai: From llms to world models","author":"Feng Tongtong","year":"2025","unstructured":"Tongtong Feng, Xin Wang, Yu-Gang Jiang, and Wenwu Zhu. 2025. Embodied ai: From llms to world models. IEEE CIRCUITS AND SYSTEMS MAGAZINE (2025)."},{"key":"e_1_3_2_1_25_1","volume-title":"Verena Rieser, Hasan Iqbal, Nenad Toma\u0161ev, Ira Ktena, Zachary Kenton, Mikel Rodriguez, et al.","author":"Gabriel Iason","year":"2024","unstructured":"Iason Gabriel, Arianna Manzini, Geoff Keeling, Lisa Anne Hendricks, Verena Rieser, Hasan Iqbal, Nenad Toma\u0161ev, Ira Ktena, Zachary Kenton, Mikel Rodriguez, et al., 2024. The ethics of advanced AI assistants. arXiv preprint arXiv:2404.16244 (2024)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16925"},{"key":"e_1_3_2_1_27_1","first-page":"2","article-title":"How human-AI feedback loops alter human perceptual, emotional and social judgements","volume":"9","author":"Glickman Moshe","year":"2025","unstructured":"Moshe Glickman and Tali Sharot. 2025. How human-AI feedback loops alter human perceptual, emotional and social judgements. Nat. Hum. Behav., V9, 2 (Feb. 2025), 345-359.","journal-title":"Nat. Hum. Behav."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"16336","author":"Greenblatt Ryan","year":"2024","unstructured":"Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, and Fabien Roger. 2024. AI Control: Improving Safety Despite Intentional Subversion. In Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. V235), Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp (Eds.). PMLR, 16295-16336. https:\/\/proceedings.mlr.press\/v235\/greenblatt24a.html"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21491-2"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690226"},{"key":"e_1_3_2_1_31_1","volume-title":"Embodied red teaming for auditing robotic foundation models. arXiv preprint arXiv:2411.18676","author":"Karnik Sathwik","year":"2024","unstructured":"Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi, Yen-Chen Lin, Tsun-Hsuan Wang, Christophe Dupuy, Rahul Gupta, and Pulkit Agrawal. 2024. Embodied red teaming for auditing robotic foundation models. arXiv preprint arXiv:2411.18676 (2024)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Noam Kolt Nicholas Caputo Jack Boeglin Cullen O'Keefe Rishi Bommasani Stephen Casper Mariano-Florentino Cu\u00e9llar Noah Feldman Iason Gabriel Gillian K Hadfield et al. 2026. Legal Alignment for Safe and Ethical AI. arXiv preprint arXiv:2601.04175 (2026).","DOI":"10.2139\/ssrn.6036657"},{"key":"e_1_3_2_1_33_1","volume-title":"V521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature, V521, 7553 (2015), 436-444."},{"key":"e_1_3_2_1_34_1","unstructured":"Jiashuo Liu Zheyan Shen Yue He Xingxuan Zhang Renzhe Xu Han Yu and Peng Cui. 2023. Towards Out-Of-Distribution Generalization: A Survey. arXiv:2108.13624 [cs.LG] https:\/\/arxiv.org\/abs\/2108.13624"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599454"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1561\/1100000083"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44206-023-00074-y"},{"key":"e_1_3_2_1_38_1","volume-title":"V28, 1","author":"Monahan George E","year":"1982","unstructured":"George E Monahan. 1982. State of the art\u2014a survey of partially observable Markov decision processes: theory, models, and algorithms. Management science, V28, 1 (1982), 1-16."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713301"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray John Schulman Jacob Hilton Fraser Kelton Luke Miller Maddie Simens Amanda Askell Peter Welinder Paul Christiano Jan Leike and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. arXiv:2203.02155 [cs.CL] https:\/\/arxiv.org\/abs\/2203.02155","DOI":"10.52202\/068431-2011"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3586183.3606763"},{"key":"e_1_3_2_1_42_1","unstructured":"Ambreesh Parthasarathy Aditya Phalnikar Ameen Jauhar Dhruv Somayajula Gokul S Krishnan and Balaraman Ravindran. 2024. Participatory Approaches in AI Development and Governance: A Principled Approach. arXiv:2407.13100 [cs.CY] https:\/\/arxiv.org\/abs\/2407.13100"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12167"},{"key":"e_1_3_2_1_44_1","first-page":"334","volume-title":"Proceedings of machine learning and systems, V1","author":"Polyzotis Neoklis","year":"2019","unstructured":"Neoklis Polyzotis, Martin Zinkevich, Sudip Roy, Eric Breck, and Steven Whang. 2019. Data validation for machine learning. Proceedings of machine learning and systems, V1 (2019), 334-347."},{"key":"e_1_3_2_1_45_1","volume-title":"Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GMjkK2CKx5","author":"Prinster Drew","year":"2025","unstructured":"Drew Prinster, Xing Han, Anqi Liu, and Suchi Saria. 2025. WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GMjkK2CKx5"},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, J. Vanschoren and S. Yeung (Eds.)","volume":"1","author":"Raji Deborah","year":"2021","unstructured":"Deborah Raji, Emily Denton, Emily M. Bender, Alex Hanna, and Amandalynne Paullada. 2021. AI and the Everything in the Whole Wide World Benchmark. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, J. Vanschoren and S. Yeung (Eds.), Vol. V1. https:\/\/datasets-benchmarks-proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/084b6fbb10729ed4da8c3d3f5a3ae7c9-Paper-round2.pdf"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372873"},{"key":"e_1_3_2_1_48_1","first-page":"576","article-title":"Game-theoretic statistics and safe anytime-valid inference","volume":"38","author":"Ramdas Aaditya","year":"2023","unstructured":"Aaditya Ramdas, Peter Gr\u00fcnwald, Vladimir Vovk, and Glenn Shafer. 2023. Game-theoretic statistics and safe anytime-valid inference. Statist. Sci., V38, 4 (2023), 576-601.","journal-title":"Statist. Sci."},{"key":"e_1_3_2_1_49_1","unstructured":"Aaditya Ramdas Johannes Ruf Martin Larsson and Wouter Koolen. 2022. Admissible anytime-valid sequential inference must rely on nonnegative martingales. arXiv:2009.03167 [math.ST] https:\/\/arxiv.org\/abs\/2009.03167"},{"key":"e_1_3_2_1_50_1","unstructured":"Anita Rao. 2026. Challenges to the monitoring of deployed AI systems. Technical Report. National Institute of Standards and Technology Gaithersburg MD."},{"key":"e_1_3_2_1_51_1","volume-title":"Chiara Sabatti, and Emmanuel Cand` es.","author":"Romano Yaniv","year":"2020","unstructured":"Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Cand` es. 2020. With Malice Toward None: Assessing Uncertainty via Equalized Coverage. Harvard Data Science Review, V2, 2 (apr 30 2020). https:\/\/hdsr.mitpress.mit.edu\/pub\/qedrwcz3."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445518"},{"key":"e_1_3_2_1_53_1","unstructured":"Christian Sandvig Kevin Hamilton Karrie Karahalios and C\u00e9dric Langbort. 2014. Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms. https:\/\/api.semanticscholar.org\/CorpusID:15686114"},{"key":"e_1_3_2_1_54_1","volume-title":"V28","author":"Sculley David","year":"2015","unstructured":"David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems, V28 (2015)."},{"key":"e_1_3_2_1_55_1","unstructured":"Aaditya Singh Adam Fry Adam Perelman Adam Tart Adi Ganesh Ahmed El-Kishky Aidan McLaughlin Aiden Low AJ Ostrow Akhila Ananthram et al. 2025. Openai gpt-5 system card. arXiv preprint arXiv:2601.03267 (2025)."},{"key":"e_1_3_2_1_56_1","volume-title":"misleading or confusing: How humans perceive fundamental building blocks of artificial intelligence explanations. arXiv preprint arXiv:2303.00934","author":"Small Edward","year":"2023","unstructured":"Edward Small, Yueqing Xuan, Danula Hettiachchi, and Kacper Sokol. 2023. Helpful, misleading or confusing: How humans perceive fundamental building blocks of artificial intelligence explanations. arXiv preprint arXiv:2303.00934 (2023)."},{"key":"e_1_3_2_1_57_1","volume-title":"8th Annual Learning for Dynamics & Control Conference.","author":"Srinivasan Anutam","year":"2026","unstructured":"Anutam Srinivasan, Antoine Leeman, and Glen Chou. 2026 a. Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis. In 8th Annual Learning for Dynamics & Control Conference."},{"key":"e_1_3_2_1_58_1","unstructured":"Anutam Srinivasan Aditya T. Vadlamani Amin Meghrazi and Srinivasan Parthasarathy. 2026 b. FedCF: Fair Federated Conformal Prediction. https:\/\/openreview.net\/forum?id=6rCsaBOQON"},{"key":"e_1_3_2_1_59_1","volume-title":"Development and the Rule of Law","author":"Democracy Lab Stanford Deliberative","year":"2026","unstructured":"Stanford Deliberative Democracy Lab, and Center on Democracy, Development and the Rule of Law. 2026. Industry-Wide Forum: Overall Summary. Summary Report. Stanford University. In partnership with Meta, Oracle, DoorDash, PayPal, Cohere, and Microsoft."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Elham Tabassi. 2023. Artificial intelligence risk management Framework (AI RMF 1.0). Technical Report. National Institute of Standards and Technology (U.S.) Gaithersburg MD.","DOI":"10.6028\/NIST.AI.100-1"},{"key":"e_1_3_2_1_61_1","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M Dai Anja Hauth Katie Millican et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)."},{"key":"e_1_3_2_1_62_1","volume-title":"The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=xiQNfYl33p","author":"Vadlamani Aditya T.","year":"2025","unstructured":"Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker, Ali Payani, and Srinivasan Parthasarathy. 2025. A Generic Framework for Conformal Fairness. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=xiQNfYl33p"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3174014"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/1062391"},{"key":"e_1_3_2_1_65_1","unstructured":"WIRED. [n.d.]. Sun Called on Java Claims. https:\/\/www.wired.com\/1997\/11\/sun-called-on-java-claims\/"},{"key":"e_1_3_2_1_66_1","volume-title":"Statistical foundations of actuarial learning and its applications","author":"W\u00fcthrich Mario V","unstructured":"Mario V W\u00fcthrich and Michael Merz. 2023. Statistical foundations of actuarial learning and its applications. Springer."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1920"},{"key":"e_1_3_2_1_68_1","volume-title":"International Conference on Machine Learning. PMLR, 24929-24962","author":"Yan Tom","year":"2022","unstructured":"Tom Yan and Chicheng Zhang. 2022. Active fairness auditing. In International Conference on Machine Learning. PMLR, 24929-24962."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:31:31Z","timestamp":1786066291000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3818648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":68,"alternative-id":["10.1145\/3770855.3818648","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3818648","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}