{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T21:49:26Z","timestamp":1771710566429,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":80,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation Graduate Research Fellowship","award":["DGE- 1745016"],"award-info":[{"award-number":["DGE- 1745016"]}]},{"name":"Center for Advancing Safety of Machine Intelligence (CASMI) at Northwestern University"},{"name":"Carnegie Mellon University Block Center for Technology and Society","award":["53680.1.5007718"],"award-info":[{"award-number":["53680.1.5007718"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,12]]},"DOI":"10.1145\/3593013.3594101","type":"proceedings-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T14:40:46Z","timestamp":1686580846000},"page":"1584-1598","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Counterfactual Prediction Under Outcome Measurement Error"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3566-9429","authenticated-orcid":false,"given":"Luke","family":"Guerdan","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-9921","authenticated-orcid":false,"given":"Amanda","family":"Coston","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6730-922X","authenticated-orcid":false,"given":"Kenneth","family":"Holstein","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8125-8227","authenticated-orcid":false,"given":"Zhiwei Steven","family":"Wu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2014.975555"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2638588"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445877"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022873112823"},{"key":"e_1_3_2_2_5_1","volume-title":"Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics","author":"Begg Colin B","year":"1983","unstructured":"Colin B Begg and Robert A Greenes. 1983. Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics (1983), 207\u2013215."},{"key":"e_1_3_2_2_6_1","volume-title":"Latent Variable Models.Learning in graphical models 371","author":"Bishop Christopher M","year":"1998","unstructured":"Christopher M Bishop. 1998. Latent Variable Models.Learning in graphical models 371 (1998)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514094.3534184"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.p20161029"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17363"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.p20171038"},{"key":"e_1_3_2_2_11_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Chou Yu-Ting","year":"2020","unstructured":"Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, and Masashi Sugiyama. 2020. Unbiased risk estimators can mislead: A case study of learning with complementary labels. In International Conference on Machine Learning. PMLR, 1929\u20131938."},{"key":"e_1_3_2_2_12_1","first-page":"4150","article-title":"Counterfactual predictions under runtime confounding","volume":"33","author":"Coston Amanda","year":"2020","unstructured":"Amanda Coston, Edward Kennedy, and Alexandra Chouldechova. 2020. Counterfactual predictions under runtime confounding. Advances in Neural Information Processing Systems 33 (2020), 4150\u20134162.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372851"},{"key":"e_1_3_2_2_14_1","volume-title":"First IEEE Conference on Secure and Trustworthy Machine Learning","author":"Coston Amanda Lee","year":"2022","unstructured":"Amanda Lee Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, and Hoda Heidari. 2022. A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. First IEEE Conference on Secure and Trustworthy Machine Learning (2022)."},{"key":"e_1_3_2_2_15_1","volume-title":"Leveraging expert consistency to improve algorithmic decision support. arXiv preprint arXiv:2101.09648","author":"De-Arteaga Maria","year":"2021","unstructured":"Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova. 2021. Leveraging expert consistency to improve algorithmic decision support. arXiv preprint arXiv:2101.09648 (2021)."},{"key":"e_1_3_2_2_16_1","volume-title":"Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment. arXiv preprint arXiv:2201.07072","author":"Denteh Augustine","year":"2022","unstructured":"Augustine Denteh and Helge Liebert. 2022. Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment. arXiv preprint arXiv:2201.07072 (2022)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/10888438.2021.1998067"},{"key":"e_1_3_2_2_18_1","volume-title":"Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems. The international journal of biostatistics 9, 2","author":"D\u00edaz Iv\u00e1n","year":"2013","unstructured":"Iv\u00e1n D\u00edaz and Mark J van der Laan. 2013. Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems. The international journal of biostatistics 9, 2 (2013), 149\u2013160."},{"key":"e_1_3_2_2_19_1","volume-title":"Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown. Preventive veterinary medicine 45, 1-2","author":"En\u00f8e Claes","year":"2000","unstructured":"Claes En\u00f8e, Marios P Georgiadis, and Wesley O Johnson. 2000. Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown. Preventive veterinary medicine 45, 1-2 (2000), 61\u201381."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1742-1241.2007.01423.x"},{"key":"e_1_3_2_2_21_1","volume-title":"The Oregon health insurance experiment: evidence from the first year. The Quarterly journal of economics 127, 3","author":"Finkelstein Amy","year":"2012","unstructured":"Amy Finkelstein, Sarah Taubman, Bill Wright, Mira Bernstein, Jonathan Gruber, Joseph P Newhouse, Heidi Allen, Katherine Baicker, and Oregon Health Study Group. 2012. The Oregon health insurance experiment: evidence from the first year. The Quarterly journal of economics 127, 3 (2012), 1057\u20131106."},{"key":"e_1_3_2_2_22_1","unstructured":"Noam Finkelstein Roy Adams Suchi Saria and Ilya Shpitser. 2021. Partial identifiability in discrete data with measurement error. In Uncertainty in Artificial Intelligence. PMLR 1798\u20131808."},{"key":"e_1_3_2_2_23_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2325\u20132336","author":"Fogliato Riccardo","year":"2020","unstructured":"Riccardo Fogliato, Alexandra Chouldechova, and Max G\u2019Sell. 2020. Fairness evaluation in presence of biased noisy labels. In International Conference on Artificial Intelligence and Statistics. PMLR, 2325\u20132336."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462538"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10742-018-0192-5"},{"key":"e_1_3_2_2_26_1","volume-title":"A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406","author":"Han Bo","year":"2020","unstructured":"Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W Tsang, James T Kwok, and Masashi Sugiyama. 2020. A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406 (2020)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_2_2_28_1","volume-title":"Estimating the error rates of diagnostic tests. Biometrics","author":"Hui Sui L","year":"1980","unstructured":"Sui L Hui and Steven D Walter. 1980. Estimating the error rates of diagnostic tests. Biometrics (1980), 167\u2013171."},{"key":"e_1_3_2_2_29_1","volume-title":"Using Machine Learning Explainability Methods to Personalize Interventions for Students","author":"Hur Paul","year":"2022","unstructured":"Paul Hur, HaeJin Lee, Suma Bhat, and Nigel Bosch. 2022. Using Machine Learning Explainability Methods to Personalize Interventions for Students.International Educational Data Mining Society (2022)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445901"},{"key":"e_1_3_2_2_31_1","volume-title":"Generalization bounds and representation learning for estimation of potential outcomes and causal effects. arXiv preprint arXiv:2001.07426","author":"Johansson Fredrik D","year":"2020","unstructured":"Fredrik D Johansson, Uri Shalit, Nathan Kallus, and David Sontag. 2020. Generalization bounds and representation learning for estimation of potential outcomes and causal effects. arXiv preprint arXiv:2001.07426 (2020)."},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Machine Learning. PMLR, 2439\u20132448","author":"Kallus Nathan","year":"2018","unstructured":"Nathan Kallus and Angela Zhou. 2018. Residual unfairness in fair machine learning from prejudiced data. In International Conference on Machine Learning. PMLR, 2439\u20132448."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3517439"},{"key":"e_1_3_2_2_34_1","volume-title":"Semiparametric doubly robust targeted double machine learning: a review. arXiv preprint arXiv:2203.06469","author":"Kennedy Edward H","year":"2022","unstructured":"Edward H Kennedy. 2022. Semiparametric doubly robust targeted double machine learning: a review. arXiv preprint arXiv:2203.06469 (2022)."},{"key":"e_1_3_2_2_35_1","volume-title":"Human decisions and machine predictions. The quarterly journal of economics 133, 1","author":"Kleinberg Jon","year":"2018","unstructured":"Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. Human decisions and machine predictions. The quarterly journal of economics 133, 1 (2018), 237\u2013293."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.p20151023"},{"key":"e_1_3_2_2_37_1","unstructured":"Candace Kruttschnitt William D Kalsbeek Carol C House 2014. Estimating the incidence of rape and sexual assault. (2014)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098066"},{"key":"e_1_3_2_2_40_1","volume-title":"Evaluating the econometric evaluations of training programs with experimental data. The American economic review","author":"LaLonde Robert J","year":"1986","unstructured":"Robert J LaLonde. 1986. Evaluating the econometric evaluations of training programs with experimental data. The American economic review (1986), 604\u2013620."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"e_1_3_2_2_42_1","volume-title":"Measuring crime: Behind the statistics","author":"Lohr Sharon","unstructured":"Sharon Lohr. 2019. Measuring crime: Behind the statistics. Chapman and Hall\/CRC."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1177\/00031224211004187"},{"key":"e_1_3_2_2_44_1","volume-title":"International conference on machine learning. PMLR, 125\u2013134","author":"Menon Aditya","year":"2015","unstructured":"Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, and Bob Williamson. 2015. Learning from corrupted binary labels via class-probability estimation. In International conference on machine learning. PMLR, 125\u2013134."},{"key":"e_1_3_2_2_45_1","volume-title":"Does machine learning automate moral hazard and error?American Economic Review 107, 5","author":"Mullainathan Sendhil","year":"2017","unstructured":"Sendhil Mullainathan and Ziad Obermeyer. 2017. Does machine learning automate moral hazard and error?American Economic Review 107, 5 (2017), 476\u2013480."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1257\/pandp.20211078"},{"key":"e_1_3_2_2_47_1","volume-title":"Learning with noisy labels. Advances in neural information processing systems 26","author":"Natarajan Nagarajan","year":"2013","unstructured":"Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. 2013. Learning with noisy labels. Advances in neural information processing systems 26 (2013)."},{"key":"e_1_3_2_2_48_1","volume-title":"Vcnet and functional targeted regularization for learning causal effects of continuous treatments. arXiv preprint arXiv:2103.07861","author":"Nie Lizhen","year":"2021","unstructured":"Lizhen Nie, Mao Ye, Qiang Liu, and Dan Nicolae. 2021. Vcnet and functional targeted regularization for learning causal effects of continuous treatments. arXiv preprint arXiv:2103.07861 (2021)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"e_1_3_2_2_50_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447\u2013453."},{"key":"e_1_3_2_2_51_1","volume-title":"Epidemiology of heart failure. Heart Failure","author":"Orso Francesco","year":"2017","unstructured":"Francesco Orso, Gianna Fabbri, and Aldo Pietro Maggioni. 2017. Epidemiology of heart failure. Heart Failure (2017), 15\u201333."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_2_53_1","volume-title":"Causal inference in statistics: An overview. Statistics surveys 3","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Causal inference in statistics: An overview. Statistics surveys 3 (2009), 96\u2013146."},{"key":"e_1_3_2_2_54_1","volume-title":"International Conference on Machine Learning. PMLR, 7599\u20137609","author":"Perdomo Juan","year":"2020","unstructured":"Juan Perdomo, Tijana Zrnic, Celestine Mendler-D\u00fcnner, and Moritz Hardt. 2020. Performative prediction. In International Conference on Machine Learning. PMLR, 7599\u20137609."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533158"},{"key":"e_1_3_2_2_56_1","volume-title":"Counterfactual Risk Assessments under Unmeasured Confounding. arXiv preprint arXiv:2212.09844","author":"Rambachan Ashesh","year":"2022","unstructured":"Ashesh Rambachan, Amanda Coston, and Edward Kennedy. 2022. Counterfactual Risk Assessments under Unmeasured Confounding. arXiv preprint arXiv:2212.09844 (2022)."},{"key":"e_1_3_2_2_57_1","volume-title":"Conference on Learning Theory. PMLR, 2624\u20132651","author":"Reeve Henry","year":"2019","unstructured":"Henry Reeve 2019. Classification with unknown class-conditional label noise on non-compact feature spaces. In Conference on Learning Theory. PMLR, 2624\u20132651."},{"key":"e_1_3_2_2_58_1","unstructured":"Fred S Roberts. 1985. Measurement theory. (1985)."},{"key":"e_1_3_2_2_59_1","volume-title":"A new approach to causal inference in mortality studies with a sustained exposure period\u2014application to control of the healthy worker survivor effect. Mathematical modelling 7, 9-12","author":"Robins James","year":"1986","unstructured":"James Robins. 1986. A new approach to causal inference in mortality studies with a sustained exposure period\u2014application to control of the healthy worker survivor effect. Mathematical modelling 7, 9-12 (1986), 1393\u20131512."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_2_2_61_1","volume-title":"Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of educational Psychology 66, 5","author":"Rubin Donald B","year":"1974","unstructured":"Donald B Rubin. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of educational Psychology 66, 5 (1974), 688."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Henri C Schouwenburg. 2004. Procrastination in Academic Settings: General Introduction. (2004).","DOI":"10.1037\/10808-000"},{"key":"e_1_3_2_2_64_1","unstructured":"Clayton Scott. 2015. A rate of convergence for mixture proportion estimation with application to learning from noisy labels. In Artificial Intelligence and Statistics. PMLR 838\u2013846."},{"key":"e_1_3_2_2_65_1","volume-title":"Conference on learning theory. PMLR, 489\u2013511","author":"Scott Clayton","year":"2013","unstructured":"Clayton Scott, Gilles Blanchard, and Gregory Handy. 2013. Classification with asymmetric label noise: Consistency and maximal denoising. In Conference on learning theory. PMLR, 489\u2013511."},{"key":"e_1_3_2_2_66_1","volume-title":"International Conference on Machine Learning. PMLR, 3076\u20133085","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit, Fredrik D Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning. PMLR, 3076\u20133085."},{"key":"e_1_3_2_2_67_1","volume-title":"Adapting neural networks for the estimation of treatment effects. Advances in neural information processing systems 32","author":"Shi Claudia","year":"2019","unstructured":"Claudia Shi, David Blei, and Victor Veitch. 2019. Adapting neural networks for the estimation of treatment effects. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(00)00115-4"},{"key":"e_1_3_2_2_69_1","unstructured":"Patrick E Shrout and Sean P Lane. 2012. Psychometrics. (2012)."},{"key":"e_1_3_2_2_70_1","volume-title":"Causal inference with measurement error in outcomes: Bias analysis and estimation methods. Statistical methods in medical research 28, 7","author":"Shu Di","year":"2019","unstructured":"Di Shu and Grace Y Yi. 2019. Causal inference with measurement error in outcomes: Bias analysis and estimation methods. Statistical methods in medical research 28, 7 (2019), 2049\u20132068."},{"key":"e_1_3_2_2_71_1","volume-title":"Does matching overcome LaLonde\u2019s critique of nonexperimental estimators?Journal of econometrics 125, 1-2","author":"Smith Jeffrey A","year":"2005","unstructured":"Jeffrey A Smith and Petra E Todd. 2005. Does matching overcome LaLonde\u2019s critique of nonexperimental estimators?Journal of econometrics 125, 1-2 (2005), 305\u2013353."},{"key":"e_1_3_2_2_72_1","volume-title":"motivating\u2019and fired. The Washington Post 6","author":"Turque Bill","year":"2012","unstructured":"Bill Turque. 2012. Creative... motivating\u2019and fired. The Washington Post 6 (2012)."},{"key":"e_1_3_2_2_73_1","unstructured":"Brendan Van Rooyen 2015. Machine learning via transitions. (2015)."},{"key":"e_1_3_2_2_74_1","volume-title":"Learning with symmetric label noise: The importance of being unhinged. Advances in neural information processing systems 28","author":"Rooyen Brendan Van","year":"2015","unstructured":"Brendan Van Rooyen, Aditya Menon, and Robert C Williamson. 2015. Learning with symmetric label noise: The importance of being unhinged. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1016\/0895-4356(88)90110-2"},{"key":"e_1_3_2_2_76_1","volume-title":"Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy. Available at SSRN","author":"Wang Angelina","year":"2022","unstructured":"Angelina Wang, Sayash Kapoor, Solon Barocas, and Arvind Narayanan. 2022. Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy. Available at SSRN (2022)."},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445915"},{"key":"e_1_3_2_2_78_1","first-page":"7597","article-title":"Part-dependent label noise: Towards instance-dependent label noise","volume":"33","author":"Xia Xiaobo","year":"2020","unstructured":"Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama. 2020. Part-dependent label noise: Towards instance-dependent label noise. Advances in Neural Information Processing Systems 33 (2020), 7597\u20137610.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_79_1","volume-title":"Are anchor points really indispensable in label-noise learning?Advances in Neural Information Processing Systems 32","author":"Xia Xiaobo","year":"2019","unstructured":"Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama. 2019. Are anchor points really indispensable in label-noise learning?Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1515\/dx-2014-0069"}],"event":{"name":"FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency","location":"Chicago IL USA","acronym":"FAccT '23"},"container-title":["2023 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594101","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3593013.3594101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:19Z","timestamp":1750178239000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,12]]},"references-count":80,"alternative-id":["10.1145\/3593013.3594101","10.1145\/3593013"],"URL":"https:\/\/doi.org\/10.1145\/3593013.3594101","relation":{},"subject":[],"published":{"date-parts":[[2023,6,12]]},"assertion":[{"value":"2023-06-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}