{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T22:19:23Z","timestamp":1769984363790,"version":"3.49.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"crossref","award":["AISG2-PhD-2021-08-022T"],"award-info":[{"award-number":["AISG2-PhD-2021-08-022T"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Cyber Security Agency under its National Cybersecurity R&D Programme","award":["NCRP25-P04-TAICeN"],"award-info":[{"award-number":["NCRP25-P04-TAICeN"]}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education, Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Academic Research Tier 3","award":["MOET32020-0004"],"award-info":[{"award-number":["MOET32020-0004"]}]},{"name":"A*STAR Centre for Frontier AI Research"},{"name":"National Research Foundation Singapore and the National Research Foundation, Singapore","award":["AISG2-RP-2020-019"],"award-info":[{"award-number":["AISG2-RP-2020-019"]}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"crossref"}]},{"name":"DSO National Laboratories"},{"name":"AI Singapore Programme","award":["AISG2-GC-2023-008"],"award-info":[{"award-number":["AISG2-GC-2023-008"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2021-02549, RGPAS-2021-00034, DGECR-2021-00019"],"award-info":[{"award-number":["RGPIN-2021-02549, RGPAS-2021-00034, DGECR-2021-00019"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Deep Neural Networks (DNNs) have achieved tremendous success in many applications, while it has been demonstrated that DNNs can exhibit some undesirable behaviors on concerns such as robustness, privacy, and other trustworthiness issues. Among them, fairness (i.e., non-discrimination) is one important property, especially when they are applied to some sensitive applications (e.g., finance and employment). However, DNNs easily learn spurious correlations between protected attributes (e.g., age, gender, race) and the classification task and develop discriminatory behaviors if the training data is imbalanced. Such discriminatory decisions in sensitive applications would introduce severe social impacts. To expose potential discrimination problems in DNNs before putting them in use, some testing techniques have been proposed to identify the discriminatory instances (i.e., instances that show defined discrimination\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n            ). However, how to repair DNNs after detecting such discrimination is still challenging. Existing techniques mainly rely on retraining on a large number of discriminatory instances generated by testing methods, which requires huge time overhead and makes the repairing inefficient.\n          <\/jats:p>\n          <jats:p>\n            In this work, we propose the method\n            <jats:italic>Faire<\/jats:italic>\n            to effectively and efficiently repair the fairness issues of DNNs, without using additional data (e.g., discriminatory instances). Our basic idea is inspired by the traditional program repair method that synthesizes proper condition checking. To repair traditional programs, a typical method is to localize the program defects and repair the program logic by adding condition checking. Similarly, for DNNs, we try to understand the unfair logic and reformulate it with well-designed condition checking. In this article, we synthesize the condition that can reduce the effect of features relevant to the protected attributes in the DNN. Specifically, we first perform the neuron-based analysis and check the functionalities of neurons to identify neurons whose outputs could be regarded as features relevant to protected attributes and original tasks. Then a new condition layer is added after each hidden layer to penalize neurons that are accountable for the protected features (i.e., intermediate features relevant to protected attributes) and promote neurons that are accountable for the non-protected features (i.e., intermediate features relevant to original tasks). In sum, the repair rate\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>2<\/jats:sup>\n            <\/jats:xref>\n            of\n            <jats:italic>Faire<\/jats:italic>\n            reaches up to more than 99%, which outperforms other methods, and the whole repairing process only takes no more than 340 s. The evaluation results demonstrate that our approach can effectively and efficiently repair the individual discriminatory instances of the target model.\n          <\/jats:p>","DOI":"10.1145\/3617168","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T12:05:08Z","timestamp":1692878708000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Faire: Repairing Fairness of Neural Networks via Neuron Condition Synthesis"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2207-1622","authenticated-orcid":false,"given":"Tianlin","family":"Li","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-6502","authenticated-orcid":false,"given":"Xiaofei","family":"Xie","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0393-3709","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0974-9299","authenticated-orcid":false,"given":"Qing","family":"Guo","sequence":"additional","affiliation":[{"name":"IHPC and CFAR, Agency for Science, Technology and Research, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4224-1318","authenticated-orcid":false,"given":"Aishan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-2420","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Alberta, Canada and The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7300-9215","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Sci-Tech University, China, and Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https:\/\/www.tensorflow.org\/Software available from tensorflow.org."},{"key":"e_1_3_2_3_2","unstructured":"Aniya Aggarwal Pranay Lohia Seema Nagar Kuntal Dey and Diptikalyan Saha. 2018. Automated test generation to detect individual discrimination in AI models. Retrieved from http:\/\/arxiv.org\/abs\/1809.03260"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338937"},{"key":"e_1_3_2_5_2","article-title":"Concrete problems in AI safety","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Man\u00e9. 2016. Concrete problems in AI safety. Retrieved from https:\/\/arXiv:1606.06565","journal-title":"R"},{"key":"e_1_3_2_6_2","unstructured":"R FairRepair"},{"key":"e_1_3_2_7_2","article-title":"Invariant risk minimization","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. Retrieved from https:\/\/arXiv:1907.02893","journal-title":"R"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"e_1_3_2_9_2","unstructured":"Hyojin Bahng Sanghyuk Chun Sangdoo Yun Jaegul Choo and Seong Joon Oh. 2019. Learning de-biased representations with biased representations. Retrieved from http:\/\/arxiv.org\/abs\/1910.02806"},{"key":"e_1_3_2_10_2","unstructured":"Rachel K. E. Bellamy Kuntal Dey Michael Hind Samuel C. Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta Aleksandra Mojsilovic Seema Nagar Karthikeyan Natesan Ramamurthy John Richards Diptikalyan Saha Prasanna Sattigeri Moninder Singh Kush R. Varshney and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting Understanding and Mitigating Unwanted Algorithmic Bias. Retrieved from https:\/\/arxiv.org\/abs\/1810.01943"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Reuben Binns. 2019. On the apparent conflict between individual and group fairness. Retrieved from http:\/\/arxiv.org\/abs\/1912.06883","DOI":"10.1145\/3351095.3372864"},{"key":"e_1_3_2_12_2","volume-title":"Keras","author":"Chollet Francois","year":"2015","unstructured":"Francois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/github.com\/fchollet\/keras"},{"key":"e_1_3_2_13_2","unstructured":"Datamonsters. 2017. 10 Applications of Artificial Neural Networks in Natural Language Processing. Retrieved from https:\/\/medium.com\/@datamonsters\/artificial-neural-networks-in-natural-language-processing-bcf62aa9151a"},{"key":"e_1_3_2_14_2","unstructured":"Mengnan Du Fan Yang Na Zou and Xia Hu. 2019. Fairness in deep learning: A computational perspective. Retrieved from http:\/\/arxiv.org\/abs\/1908.08843"},{"key":"e_1_3_2_15_2","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. Retrieved from http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold and Richard S. Zemel. 2011. Fairness through awareness. Retrieved from http:\/\/arxiv.org\/abs\/1104.3913","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_17_2","unstructured":"Carnegie Endowment. 2019. The Global Expansion of AI Surveillance. Retrieved from https:\/\/carnegieendowment.org\/2019\/09\/17\/global-expansion-of-ai-surveillance-pub-79847"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Michael Feldman Sorelle Friedler John Moeller Carlos Scheidegger and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. Retrieved from https:\/\/1412.3756","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_19_2","unstructured":"Rui Feng Yang Yang Yuehan Lyu Chenhao Tan Yizhou Sun and Chunping Wang. 2019. Learning fair representations via an adversarial framework. Retrieved from http:\/\/arxiv.org\/abs\/1904.13341"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106277"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Sainyam Galhotra Yuriy Brun and Alexandra Meliou. 2017. Fairness testing: Testing software for discrimination. Retrieved from http:\/\/arxiv.org\/abs\/1709.03221.","DOI":"10.1145\/3106237.3106277"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380415"},{"key":"e_1_3_2_23_2","unstructured":"Amirata Ghorbani and James Y. Zou. 2020. Neuron shapley: Discovering the responsible neurons. Retrieved from https:\/\/arxiv.org\/abs\/2002.09815"},{"key":"e_1_3_2_24_2","volume-title":"Proceedings of the 23rd International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR\u201920)","author":"Goldberger Ben","year":"2020","unstructured":"Ben Goldberger, Guy Katz, Yossi Adi, and Joseph Keshet. 2020. Minimal modifications of deep neural networks using verification. In Proceedings of the 23rd International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR\u201920)."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-017-9314-z"},{"key":"e_1_3_2_26_2","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations.","journal-title":"Proceedings of the International Conference on Learning Representations"},{"key":"e_1_3_2_27_2","article-title":"Fairness without demographics through adversarially reweighted learning","author":"Lahoti Preethi","year":"2020","unstructured":"Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi. 2020. Fairness without demographics through adversarially reweighted learning. Retrieved from https:\/\/arXiv:2006.13114","journal-title":"R"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_29_2","unstructured":"Tianlin Li Qing Guo Aishan Liu Mengnan Du Zhiming Li and Yang Liu. 2023. FAIRER: Fairness as Decision Rationale Alignment. Retrieved from https:\/\/2306.15299"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.08.043"},{"key":"e_1_3_2_31_2","unstructured":"Suyun Liu and Lu\u00eds Nunes Vicente. 2020. Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach. Retrieved from https:\/\/arxiv.org\/abs\/2008.01132"},{"key":"e_1_3_2_32_2","unstructured":"Haotian Ma Yinqing Zhang Fan Zhou and Quanshi Zhang. 2019. Quantifying layerwise information discarding of neural networks. Retrieved from http:\/\/arxiv.org\/abs\/1906.04109"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238202"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.2594510"},{"key":"e_1_3_2_35_2","volume-title":"IBM Abandons \u201cBiased\u201d Facial Recognition Tech","author":"News BBC","year":"2020","unstructured":"BBC News. 2020. IBM Abandons \u201cBiased\u201d Facial Recognition Tech. Retrieved from https:\/\/www.bbc.co.uk\/news\/technology-52978191"},{"key":"e_1_3_2_36_2","volume-title":"AI at Work: Staff \u201cHired and Fired by Algorithm.\u201d","author":"News BBC","year":"2021","unstructured":"BBC News. 2021. AI at Work: Staff \u201cHired and Fired by Algorithm.\u201d Retrieved from https:\/\/www.bbc.com\/news\/technology-56515827"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Luca Oneto Michele Donini Amon Elders and Massimiliano Pontil. 2020. Taking Advantage of Multitask Learning for Fair Classification. Retrieved from https:\/\/1810.08683","DOI":"10.1145\/3306618.3314255"},{"key":"e_1_3_2_38_2","unstructured":"Hua Qi Zhijie Wang Qing Guo Jianlang Chen Felix Juefei-Xu Lei Ma and Jianjun Zhao. 2021. ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks. Retrieved from https:\/\/2111.13330"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00918"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Mhd Hasan Sarhan Nassir Navab Abouzar Eslami and Shadi Albarqouni. 2020. Fairness by learning orthogonal disentangled representations. Retrieved from https:\/\/arxiv.org\/abs\/2003.05707","DOI":"10.1007\/978-3-030-58526-6_44"},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","unstructured":"Florian Schroff Dmitry Kalenichenko and James Philbin. 2015. FaceNet: A unified embedding for face recognition and clustering. Retrieved from http:\/\/arxiv.org\/abs\/1503.03832","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_42_2","unstructured":"Avanti Shrikumar Peyton Greenside and Anshul Kundaje. 2017. Learning important features through propagating activation differences. Retrieved from http:\/\/arxiv.org\/abs\/1704.02685"},{"key":"e_1_3_2_43_2","unstructured":"Jeongju Sohn Sungmin Kang and Shin Yoo. 2019. Search-based repair of deep neural networks. Retrieved from http:\/\/arxiv.org\/abs\/1912.12463"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454064"},{"key":"e_1_3_2_45_2","unstructured":"TechCrunch. 2020. Nearly 70% of U.S. smart speaker owners use Amazon Echo devices. Retrieved from https:\/\/techcrunch.com\/2020\/02\/10\/nearly-70-of-u-s-smart-speaker-owners-use-amazon-echo-devices\/"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238165"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00541"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00928"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00894"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Yisong Xiao Aishan Liu Tianlin Li and Xianglong Liu. 2023. Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing. Retrieved from https:\/\/2305.11602","DOI":"10.1145\/3597926.3598099"},{"key":"e_1_3_2_51_2","first-page":"11383","volume-title":"Proceedings of the International Conference on Machine Learning (ICML\u201921)","author":"Xie Xiaofei","year":"2021","unstructured":"Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, and Xinyu Xing. 2021. RNNrepair: Automatic RNN repair via model-based analysis. In Proceedings of the International Conference on Machine Learning (ICML\u201921). PMLR, 11383\u201311392."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490489"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2017.45"},{"key":"e_1_3_2_54_2","unstructured":"Tian Xu Jennifer White Sinan Kalkan and Hatice Gunes. 2020. Investigating bias and fairness in facial expression recognition. Retrieved from https:\/\/arxiv.org\/abs\/2007.10075"},{"key":"e_1_3_2_55_2","unstructured":"Bing Yu Hua Qi Qing Guo Felix Juefei-Xu Xiaofei Xie Lei Ma and Jianjun Zhao. 2020. DeepRepair: Style-guided repairing for DNNs in the real-world operational environment. Retrieved from https:\/\/arxiv.org\/abs\/2011.09884"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"Brian Hu Zhang Blake Lemoine and Margaret Mitchell. 2018. Mitigating unwanted biases with adversarial learning. Retrieved from http:\/\/arxiv.org\/abs\/1801.07593","DOI":"10.1145\/3278721.3278779"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3042083"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00043"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464820"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380331"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409676"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617168","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3617168","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:07Z","timestamp":1750178167000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,31]]}},"alternative-id":["10.1145\/3617168"],"URL":"https:\/\/doi.org\/10.1145\/3617168","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]},"assertion":[{"value":"2022-02-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-12","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}