{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:16:03Z","timestamp":1763748963493,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":102,"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":"UL Research Institutes through the Center for Advancing Safety of Machine Intelligence (CASMI) at Northwestern University"},{"name":"National Science Foundation Graduate Research Fellowship Program","award":["DGE- 1745016"],"award-info":[{"award-number":["DGE- 1745016"]}]},{"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\/3593013.3594036","type":"proceedings-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T14:40:46Z","timestamp":1686580846000},"page":"688-704","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making"],"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\/0000-0002-8125-8227","authenticated-orcid":false,"given":"Zhiwei Steven","family":"Wu","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"}]}],"member":"320","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_2_1_1","DOI":"10.1145\/3442188.3445877"},{"key":"e_1_3_2_2_2_1","volume-title":"Peter WF Wilson, and William B Kannel","author":"Anderson Keaven M","year":"1991","unstructured":"Keaven M Anderson , Patricia M Odell , Peter WF Wilson, and William B Kannel . 1991 . Cardiovascular disease risk profiles. American heart journal 121, 1 (1991), 293\u2013298. Keaven M Anderson, Patricia M Odell, Peter WF Wilson, and William B Kannel. 1991. Cardiovascular disease risk profiles. American heart journal 121, 1 (1991), 293\u2013298."},{"volume-title":"Ethics of Data and Analytics","author":"Angwin Julia","unstructured":"Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias . In Ethics of Data and Analytics . Auerbach Publications , 254\u2013264. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics. Auerbach Publications, 254\u2013264.","key":"e_1_3_2_2_3_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_4_1","DOI":"10.1145\/3411764.3445717"},{"key":"e_1_3_2_2_5_1","volume-title":"It\u2019s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv preprint arXiv:2106.05498","author":"Bao Michelle","year":"2021","unstructured":"Michelle Bao , Angela Zhou , Samantha Zottola , Brian Brubach , Sarah Desmarais , Aaron Horowitz , Kristian Lum , and Suresh Venkatasubramanian . 2021. It\u2019s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv preprint arXiv:2106.05498 ( 2021 ). Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian. 2021. It\u2019s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. arXiv preprint arXiv:2106.05498 (2021)."},{"key":"e_1_3_2_2_6_1","volume-title":"Conference on fairness, accountability and transparency. PMLR, 62\u201376","author":"Barabas Chelsea","year":"2018","unstructured":"Chelsea Barabas , Madars Virza , Karthik Dinakar , Joichi Ito , and Jonathan Zittrain . 2018 . Interventions over predictions: Reframing the ethical debate for actuarial risk assessment . In Conference on fairness, accountability and transparency. PMLR, 62\u201376 . Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito, and Jonathan Zittrain. 2018. Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. In Conference on fairness, accountability and transparency. PMLR, 62\u201376."},{"unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org. http:\/\/www.fairmlbook.org.  Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org. http:\/\/www.fairmlbook.org.","key":"e_1_3_2_2_7_1"},{"key":"e_1_3_2_2_8_1","volume-title":"An introduction to sample selection bias in sociological data. American sociological review","author":"Berk Richard A","year":"1983","unstructured":"Richard A Berk . 1983. An introduction to sample selection bias in sociological data. American sociological review ( 1983 ), 386\u2013398. Richard A Berk. 1983. An introduction to sample selection bias in sociological data. American sociological review (1983), 386\u2013398."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_9_1","DOI":"10.1145\/3377325.3377498"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_10_1","DOI":"10.1145\/3449287"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_11_1","DOI":"10.1109\/ICHI.2015.26"},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society. 130\u2013143","author":"Butcher Bradley","year":"2022","unstructured":"Bradley Butcher , Chris Robinson , Miri Zilka , Riccardo Fogliato , Carolyn Ashurst , and Adrian Weller . 2022 . Racial Disparities in the Enforcement of Marijuana Violations in the US . In Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society. 130\u2013143 . Bradley Butcher, Chris Robinson, Miri Zilka, Riccardo Fogliato, Carolyn Ashurst, and Adrian Weller. 2022. Racial Disparities in the Enforcement of Marijuana Violations in the US. In Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society. 130\u2013143."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_13_1","DOI":"10.1145\/3359206"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_14_1","DOI":"10.1145\/3411763.3443435"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_15_1","DOI":"10.1609\/icwsm.v14i1.7282"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_16_1","DOI":"10.1257\/aer.p20161029"},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Machine Learning. PMLR, 2972\u20133005","author":"Charusaie Mohammad-Amin","year":"2022","unstructured":"Mohammad-Amin Charusaie , Hussein Mozannar , David Sontag , and Samira Samadi . 2022 . Sample Efficient Learning of Predictors that Complement Humans . In International Conference on Machine Learning. PMLR, 2972\u20133005 . Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, and Samira Samadi. 2022. Sample Efficient Learning of Predictors that Complement Humans. In International Conference on Machine Learning. PMLR, 2972\u20133005."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_18_1","DOI":"10.1145\/3491102.3501831"},{"key":"e_1_3_2_2_19_1","volume-title":"Conference on Fairness, Accountability and Transparency. PMLR, 134\u2013148","author":"Chouldechova Alexandra","year":"2018","unstructured":"Alexandra Chouldechova , Diana Benavides-Prado , Oleksandr Fialko , and Rhema Vaithianathan . 2018 . A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions . In Conference on Fairness, Accountability and Transparency. PMLR, 134\u2013148 . Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency. PMLR, 134\u2013148."},{"key":"e_1_3_2_2_20_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 \u2013 4162 . 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_21_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 \u2013 4162 . 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"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_22_1","DOI":"10.1145\/3351095.3372851"},{"key":"e_1_3_2_2_23_1","volume-title":"SoK: A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. First IEEE Conference on Secure and Trustworthy Machine Learning","author":"Coston Amanda Lee","year":"2023","unstructured":"Amanda Lee Coston , Anna Kawakami , Haiyi Zhu , Ken Holstein , and Hoda Heidari . 2023 . SoK: A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. First IEEE Conference on Secure and Trustworthy Machine Learning (2023). Amanda Lee Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, and Hoda Heidari. 2023. SoK: A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. First IEEE Conference on Secure and Trustworthy Machine Learning (2023)."},{"key":"e_1_3_2_2_24_1","volume-title":"Clinical versus actuarial judgment. Science 243, 4899","author":"Dawes Robyn M","year":"1989","unstructured":"Robyn M Dawes , David Faust , and Paul E Meehl . 1989. Clinical versus actuarial judgment. Science 243, 4899 ( 1989 ), 1668\u20131674. Robyn M Dawes, David Faust, and Paul E Meehl. 1989. Clinical versus actuarial judgment. Science 243, 4899 (1989), 1668\u20131674."},{"key":"e_1_3_2_2_25_1","volume-title":"Learning under selective labels in the presence of expert consistency. arXiv preprint arXiv:1807.00905","author":"De-Arteaga Maria","year":"2018","unstructured":"Maria De-Arteaga , Artur Dubrawski , and Alexandra Chouldechova . 2018. Learning under selective labels in the presence of expert consistency. arXiv preprint arXiv:1807.00905 ( 2018 ). Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova. 2018. Learning under selective labels in the presence of expert consistency. arXiv preprint arXiv:1807.00905 (2018)."},{"key":"e_1_3_2_2_26_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 ). Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova. 2021. Leveraging expert consistency to improve algorithmic decision support. arXiv preprint arXiv:2101.09648 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_27_1","DOI":"10.1145\/3287560.3287572"},{"key":"e_1_3_2_2_28_1","volume-title":"Whose ground truth? accounting for individual and collective identities underlying dataset annotation. arXiv preprint arXiv:2112.04554","author":"Denton Emily","year":"2021","unstructured":"Emily Denton , Mark D\u00edaz , Ian Kivlichan , Vinodkumar Prabhakaran , and Rachel Rosen . 2021. Whose ground truth? accounting for individual and collective identities underlying dataset annotation. arXiv preprint arXiv:2112.04554 ( 2021 ). Emily Denton, Mark D\u00edaz, Ian Kivlichan, Vinodkumar Prabhakaran, and Rachel Rosen. 2021. Whose ground truth? accounting for individual and collective identities underlying dataset annotation. arXiv preprint arXiv:2112.04554 (2021)."},{"key":"e_1_3_2_2_29_1","volume-title":"2022 ACM Conference on Fairness, Accountability, and Transparency. 1207\u20131239","author":"Diana Emily","year":"2022","unstructured":"Emily Diana , Wesley Gill , Michael Kearns , Krishnaram Kenthapadi , Aaron Roth , and Saeed Sharifi-Malvajerdi . 2022 . Multiaccurate proxies for downstream fairness . In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1207\u20131239 . Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2022. Multiaccurate proxies for downstream fairness. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1207\u20131239."},{"key":"e_1_3_2_2_30_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. 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_31_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 7, 4 ( 2016 ). William Dieterich, Christina Mendoza, and Tim Brennan. 2016. COMPAS risk scales: Demonstrating accuracy equity and predictive parity. Northpointe Inc 7, 4 (2016)."},{"key":"e_1_3_2_2_32_1","first-page":"114","article-title":"Algorithm aversion: people erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology","volume":"144","author":"Dietvorst Berkeley J","year":"2015","unstructured":"Berkeley J Dietvorst , Joseph P Simmons , and Cade Massey . 2015 . Algorithm aversion: people erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology : General 144 , 1 (2015), 114 . Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: people erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General 144, 1 (2015), 114.","journal-title":"General"},{"key":"e_1_3_2_2_33_1","volume-title":"Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. arXiv preprint arXiv:2202.08821","author":"Donahue Kate","year":"2022","unstructured":"Kate Donahue , Alexandra Chouldechova , and Krishnaram Kenthapadi . 2022. Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. arXiv preprint arXiv:2202.08821 ( 2022 ). Kate Donahue, Alexandra Chouldechova, and Krishnaram Kenthapadi. 2022. Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. arXiv preprint arXiv:2202.08821 (2022)."},{"key":"e_1_3_2_2_34_1","volume-title":"Conference on Fairness, Accountability and Transparency. PMLR, 160\u2013171","author":"Ensign Danielle","year":"2018","unstructured":"Danielle Ensign , Sorelle A Friedler , Scott Neville , Carlos Scheidegger , and Suresh Venkatasubramanian . 2018 . Runaway feedback loops in predictive policing . In Conference on Fairness, Accountability and Transparency. PMLR, 160\u2013171 . Danielle Ensign, Sorelle A Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian. 2018. Runaway feedback loops in predictive policing. In Conference on Fairness, Accountability and Transparency. PMLR, 160\u2013171."},{"key":"e_1_3_2_2_35_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 . 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_36_1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 5, CSCW2","author":"Fogliato Riccardo","year":"2021","unstructured":"Riccardo Fogliato , Alexandra Chouldechova , and Zachary Lipton . 2021 . The impact of algorithmic risk assessments on human predictions and its analysis via crowdsourcing studies . Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1\u201324. Riccardo Fogliato, Alexandra Chouldechova, and Zachary Lipton. 2021. The impact of algorithmic risk assessments on human predictions and its analysis via crowdsourcing studies. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1\u201324."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_37_1","DOI":"10.1145\/3461702.3462538"},{"key":"e_1_3_2_2_38_1","volume-title":"Classification in the presence of label noise: a survey","author":"Fr\u00e9nay Beno\u00eet","year":"2013","unstructured":"Beno\u00eet Fr\u00e9nay and Michel Verleysen . 2013. Classification in the presence of label noise: a survey . IEEE transactions on neural networks and learning systems 25, 5 ( 2013 ), 845\u2013869. Beno\u00eet Fr\u00e9nay and Michel Verleysen. 2013. Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems 25, 5 (2013), 845\u2013869."},{"key":"e_1_3_2_2_39_1","volume-title":"Impact of AI Assistance on Incidental Learning. In 27th International Conference on Intelligent User Interfaces. 794\u2013806","author":"Gajos Krzysztof Z","year":"2022","unstructured":"Krzysztof Z Gajos and Lena Mamykina . 2022 . Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning. In 27th International Conference on Intelligent User Interfaces. 794\u2013806 . Krzysztof Z Gajos and Lena Mamykina. 2022. Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning. In 27th International Conference on Intelligent User Interfaces. 794\u2013806."},{"key":"e_1_3_2_2_40_1","volume-title":"Min Kyung Lee, and Matthew Lease","author":"Gao Ruijiang","year":"2021","unstructured":"Ruijiang Gao , Maytal Saar-Tsechansky , Maria De-Arteaga , Ligong Han , Min Kyung Lee, and Matthew Lease . 2021 . Human-AI collaboration with bandit feedback. arXiv preprint arXiv:2105.10614 (2021). Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, and Matthew Lease. 2021. Human-AI collaboration with bandit feedback. arXiv preprint arXiv:2105.10614 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_41_1","DOI":"10.1145\/3491102.3502004"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_42_1","DOI":"10.1145\/3359152"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_43_1","DOI":"10.1145\/3479562"},{"key":"e_1_3_2_2_44_1","volume-title":"Clinical versus mechanical prediction: a meta-analysis.Psychological assessment 12, 1","author":"Grove William M","year":"2000","unstructured":"William M Grove , David H Zald , Boyd S Lebow , Beth E Snitz , and Chad Nelson . 2000. Clinical versus mechanical prediction: a meta-analysis.Psychological assessment 12, 1 ( 2000 ), 19. William M Grove, David H Zald, Boyd S Lebow, Beth E Snitz, and Chad Nelson. 2000. Clinical versus mechanical prediction: a meta-analysis.Psychological assessment 12, 1 (2000), 19."},{"key":"e_1_3_2_2_45_1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 4, CSCW2","author":"Halfaker Aaron","year":"2020","unstructured":"Aaron Halfaker and R Stuart Geiger . 2020 . Ores: Lowering barriers with participatory machine learning in wikipedia . Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1\u201337. Aaron Halfaker and R Stuart Geiger. 2020. Ores: Lowering barriers with participatory machine learning in wikipedia. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1\u201337."},{"key":"e_1_3_2_2_46_1","volume-title":"Measurement theory and practice","author":"Hand David J","year":"2004","unstructured":"David J Hand . 2004. Measurement theory and practice . London : Arnold ( 2004 ). David J Hand. 2004. Measurement theory and practice. London: Arnold (2004)."},{"key":"e_1_3_2_2_47_1","volume-title":"Backward baselines: Is your model predicting the past?arXiv preprint arXiv:2206.11673","author":"Hardt Moritz","year":"2022","unstructured":"Moritz Hardt and Michael P Kim . 2022. Backward baselines: Is your model predicting the past?arXiv preprint arXiv:2206.11673 ( 2022 ). Moritz Hardt and Michael P Kim. 2022. Backward baselines: Is your model predicting the past?arXiv preprint arXiv:2206.11673 (2022)."},{"volume-title":"Modern factor analysis","author":"Harman Harry H","unstructured":"Harry H Harman . 1976. Modern factor analysis . University of Chicago press. Harry H Harman. 1976. Modern factor analysis. University of Chicago press.","key":"e_1_3_2_2_48_1"},{"key":"e_1_3_2_2_49_1","volume-title":"Sample selection bias as a specification error. Econometrica: Journal of the econometric society","author":"Heckman James J","year":"1979","unstructured":"James J Heckman . 1979. Sample selection bias as a specification error. Econometrica: Journal of the econometric society ( 1979 ), 153\u2013161. James J Heckman. 1979. Sample selection bias as a specification error. Econometrica: Journal of the econometric society (1979), 153\u2013161."},{"key":"e_1_3_2_2_50_1","volume-title":"On the Effect of Information Asymmetry in Human-AI Teams. arXiv preprint arXiv:2205.01467","author":"Hemmer Patrick","year":"2022","unstructured":"Patrick Hemmer , Max Schemmer , Niklas K\u00fchl , Michael V\u00f6ssing , and Gerhard Satzger . 2022. On the Effect of Information Asymmetry in Human-AI Teams. arXiv preprint arXiv:2205.01467 ( 2022 ). Patrick Hemmer, Max Schemmer, Niklas K\u00fchl, Michael V\u00f6ssing, and Gerhard Satzger. 2022. On the Effect of Information Asymmetry in Human-AI Teams. arXiv preprint arXiv:2205.01467 (2022)."},{"key":"e_1_3_2_2_51_1","volume-title":"International Conference on Machine Learning. PMLR, 4227\u20134238","author":"Hilgard Sophie","year":"2021","unstructured":"Sophie Hilgard , Nir Rosenfeld , Mahzarin R Banaji , Jack Cao , and David Parkes . 2021 . Learning representations by humans, for humans . In International Conference on Machine Learning. PMLR, 4227\u20134238 . Sophie Hilgard, Nir Rosenfeld, Mahzarin R Banaji, Jack Cao, and David Parkes. 2021. Learning representations by humans, for humans. In International Conference on Machine Learning. PMLR, 4227\u20134238."},{"key":"e_1_3_2_2_52_1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 7, CSCW1","author":"Holstein Kenneth","year":"2023","unstructured":"Kenneth Holstein , Maria De-Arteaga , Lakshmi Tumati , and Yanghuidi Cheng . 2023 . Toward supporting perceptual complementarity in human-AI collaboration via reflection on unobservables . Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1\u201320. Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, and Yanghuidi Cheng. 2023. Toward supporting perceptual complementarity in human-AI collaboration via reflection on unobservables. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1\u201320."},{"key":"e_1_3_2_2_53_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. Sui L Hui and Steven D Walter. 1980. Estimating the error rates of diagnostic tests. Biometrics (1980), 167\u2013171."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_54_1","DOI":"10.1145\/3442188.3445901"},{"key":"e_1_3_2_2_55_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 . Nathan Kallus and Angela Zhou. 2018. Residual unfairness in fair machine learning from prejudiced data. In International Conference on Machine Learning. PMLR, 2439\u20132448."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_56_1","DOI":"10.1145\/3491102.3517439"},{"key":"e_1_3_2_2_57_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. 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."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_58_1","DOI":"10.1257\/aer.p20151023"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_59_1","DOI":"10.1609\/aaai.v33i01.3301622"},{"key":"e_1_3_2_2_60_1","volume-title":"Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies. arXiv preprint arXiv:2112.11471","author":"Lai Vivian","year":"2021","unstructured":"Vivian Lai , Chacha Chen , Q Vera Liao , Alison Smith-Renner , and Chenhao Tan . 2021. Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies. arXiv preprint arXiv:2112.11471 ( 2021 ). Vivian Lai, Chacha Chen, Q Vera Liao, Alison Smith-Renner, and Chenhao Tan. 2021. Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies. arXiv preprint arXiv:2112.11471 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_61_1","DOI":"10.1145\/3313831.3376873"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_62_1","DOI":"10.1145\/3287560.3287590"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_63_1","DOI":"10.1145\/3097983.3098066"},{"key":"e_1_3_2_2_64_1","volume-title":"Proceedings of the ACM on Human-Computer Interaction 5, CSCW2","author":"Liu Han","year":"2021","unstructured":"Han Liu , Vivian Lai , and Chenhao Tan . 2021 . Understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making . Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1\u201345. Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1\u201345."},{"key":"e_1_3_2_2_65_1","volume-title":"Predict responsibly: improving fairness and accuracy by learning to defer. Advances in Neural Information Processing Systems 31","author":"Madras David","year":"2018","unstructured":"David Madras , Toni Pitassi , and Richard Zemel . 2018. Predict responsibly: improving fairness and accuracy by learning to defer. Advances in Neural Information Processing Systems 31 ( 2018 ). David Madras, Toni Pitassi, and Richard Zemel. 2018. Predict responsibly: improving fairness and accuracy by learning to defer. Advances in Neural Information Processing Systems 31 (2018)."},{"volume-title":"Latent class analysis. Number 64","author":"McCutcheon Allan L","unstructured":"Allan L McCutcheon . 1987. Latent class analysis. Number 64 . Sage . Allan L McCutcheon. 1987. Latent class analysis. Number 64. Sage.","key":"e_1_3_2_2_66_1"},{"key":"e_1_3_2_2_67_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 . 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."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_68_1","DOI":"10.1145\/3442188.3445933"},{"key":"e_1_3_2_2_69_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Mozannar Hussein","year":"2022","unstructured":"Hussein Mozannar , Arvind Satyanarayan , and David Sontag . 2022 . Teaching humans when to defer to a classifier via exemplars . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36 . 5323\u20135331. Hussein Mozannar, Arvind Satyanarayan, and David Sontag. 2022. Teaching humans when to defer to a classifier via exemplars. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 5323\u20135331."},{"key":"e_1_3_2_2_70_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\u201380. Sendhil Mullainathan and Ziad Obermeyer. 2017. Does machine learning automate moral hazard and error?American Economic Review 107, 5 (2017), 476\u201380."},{"volume-title":"A machine learning approach to low-value health care: wasted tests, missed heart attacks and mis-predictions","author":"Mullainathan Sendhil","unstructured":"Sendhil Mullainathan and Ziad Obermeyer . 2019. A machine learning approach to low-value health care: wasted tests, missed heart attacks and mis-predictions . National Bureau of Economic Research . Sendhil Mullainathan and Ziad Obermeyer. 2019. A machine learning approach to low-value health care: wasted tests, missed heart attacks and mis-predictions. National Bureau of Economic Research.","key":"e_1_3_2_2_71_1"},{"key":"e_1_3_2_2_72_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 ). Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. 2013. Learning with noisy labels. Advances in neural information processing systems 26 (2013)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_73_1","DOI":"10.1613\/jair.1.12125"},{"key":"e_1_3_2_2_74_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. 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_75_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1093\/biomet\/ass054","article-title":"Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders","volume":"100","author":"Ogburn Elizabeth L","year":"2013","unstructured":"Elizabeth L Ogburn and Tyler J Vanderweele . 2013 . Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders . Biometrika 100 , 1 (2013), 241 \u2013 248 . Elizabeth L Ogburn and Tyler J Vanderweele. 2013. Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders. Biometrika 100, 1 (2013), 241\u2013248.","journal-title":"Biometrika"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_76_1","DOI":"10.1145\/3359204"},{"key":"e_1_3_2_2_77_1","volume-title":"Toxicity detection: Does context really matter?arXiv preprint arXiv:2006.00998","author":"Pavlopoulos John","year":"2020","unstructured":"John Pavlopoulos , Jeffrey Sorensen , Lucas Dixon , Nithum Thain , and Ion Androutsopoulos . 2020. Toxicity detection: Does context really matter?arXiv preprint arXiv:2006.00998 ( 2020 ). John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, and Ion Androutsopoulos. 2020. Toxicity detection: Does context really matter?arXiv preprint arXiv:2006.00998 (2020)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_78_1","DOI":"10.1093\/biomet\/82.4.669"},{"key":"e_1_3_2_2_79_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. Judea Pearl. 2009. Causal inference in statistics: An overview. Statistics surveys 3 (2009), 96\u2013146."},{"key":"e_1_3_2_2_80_1","volume-title":"Proceedings of the AAAI Conference on Human Computation and Crowdsourcing","volume":"7","author":"Peng Andi","year":"2019","unstructured":"Andi Peng , Besmira Nushi , Emre K\u0131c\u0131man , Kori Inkpen , Siddharth Suri , and Ece Kamar . 2019 . What you see is what you get? the impact of representation criteria on human bias in hiring . In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , Vol. 7 . 125\u2013134. Andi Peng, Besmira Nushi, Emre K\u0131c\u0131man, Kori Inkpen, Siddharth Suri, and Ece Kamar. 2019. What you see is what you get? the impact of representation criteria on human bias in hiring. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 7. 125\u2013134."},{"key":"e_1_3_2_2_81_1","volume-title":"London","author":"Pischke Steve","year":"2007","unstructured":"Steve Pischke . 2007. Lecture notes on measurement error. London School of Economics , London ( 2007 ). Steve Pischke. 2007. Lecture notes on measurement error. London School of Economics, London (2007)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_82_1","DOI":"10.1007\/s10579-020-09502-8"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_83_1","DOI":"10.1145\/3411764.3445315"},{"key":"e_1_3_2_2_84_1","volume-title":"Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice","author":"Rachatasumrit Napol","year":"2021","unstructured":"Napol Rachatasumrit and Kenneth R Koedinger . 2021. Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice . International Educational Data Mining Society ( 2021 ). Napol Rachatasumrit and Kenneth R Koedinger. 2021. Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice.International Educational Data Mining Society (2021)."},{"key":"e_1_3_2_2_85_1","volume-title":"Bias in, bias out? Evaluating the folk wisdom. arXiv preprint arXiv:1909.08518","author":"Rambachan Ashesh","year":"2019","unstructured":"Ashesh Rambachan and Jonathan Roth . 2019. Bias in, bias out? Evaluating the folk wisdom. arXiv preprint arXiv:1909.08518 ( 2019 ). Ashesh Rambachan and Jonathan Roth. 2019. Bias in, bias out? Evaluating the folk wisdom. arXiv preprint arXiv:1909.08518 (2019)."},{"unstructured":"Fred S Roberts. 1985. Measurement theory. (1985).  Fred S Roberts. 1985. Measurement theory. (1985).","key":"e_1_3_2_2_86_1"},{"key":"e_1_3_2_2_87_1","volume-title":"Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science","author":"Rosenbaum Paul R","year":"2005","unstructured":"Paul R Rosenbaum . 2005. Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science ( 2005 ). Paul R Rosenbaum. 2005. Sensitivity analysis in observational studies. Encyclopedia of statistics in behavioral science (2005)."},{"key":"e_1_3_2_2_88_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 . 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."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_89_1","DOI":"10.1145\/3442188.3445865"},{"key":"e_1_3_2_2_90_1","volume-title":"Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses.Journal of asynchronous learning networks 16, 3","author":"Smith Vernon C","year":"2012","unstructured":"Vernon C Smith , Adam Lange , and Daniel R Huston . 2012. Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses.Journal of asynchronous learning networks 16, 3 ( 2012 ), 51\u201361. Vernon C Smith, Adam Lange, and Daniel R Huston. 2012. Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses.Journal of asynchronous learning networks 16, 3 (2012), 51\u201361."},{"key":"e_1_3_2_2_91_1","volume-title":"Algorithmic risk assessment in the hands of humans. Available at SSRN 3489440","author":"Stevenson Megan T","year":"2022","unstructured":"Megan T Stevenson and Jennifer L Doleac . 2022. Algorithmic risk assessment in the hands of humans. Available at SSRN 3489440 ( 2022 ). Megan T Stevenson and Jennifer L Doleac. 2022. Algorithmic risk assessment in the hands of humans. Available at SSRN 3489440 (2022)."},{"key":"e_1_3_2_2_92_1","volume-title":"Connie Moon Sehat","author":"Stray Jonathan","year":"2022","unstructured":"Jonathan Stray , Alon Halevy , Parisa Assar , Dylan Hadfield-Menell , Craig Boutilier , Amar Ashar , Lex Beattie , Michael Ekstrand , Claire Leibowicz , Connie Moon Sehat , 2022 . Building Human Values into Recommender Systems : An Interdisciplinary Synthesis . arXiv preprint arXiv:2207.10192 (2022). Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, 2022. Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. arXiv preprint arXiv:2207.10192 (2022)."},{"key":"e_1_3_2_2_93_1","volume-title":"Investigating human+ machine complementarity for recidivism predictions. arXiv preprint arXiv:1808.09123","author":"Tan Sarah","year":"2018","unstructured":"Sarah Tan , Julius Adebayo , Kori Inkpen , and Ece Kamar . 2018. Investigating human+ machine complementarity for recidivism predictions. arXiv preprint arXiv:1808.09123 ( 2018 ). Sarah Tan, Julius Adebayo, Kori Inkpen, and Ece Kamar. 2018. Investigating human+ machine complementarity for recidivism predictions. arXiv preprint arXiv:1808.09123 (2018)."},{"key":"e_1_3_2_2_94_1","volume-title":"Allegheny family screening tool: Methodology, version 2","author":"Vaithianathan Rhema","year":"2019","unstructured":"Rhema Vaithianathan , Emily Kulick , Emily Putnam-Hornstein , and D Benavides-Prado . 2019. Allegheny family screening tool: Methodology, version 2 . Center for Social Data Analytics ( 2019 ), 1\u201322. Rhema Vaithianathan, Emily Kulick, Emily Putnam-Hornstein, and D Benavides-Prado. 2019. Allegheny family screening tool: Methodology, version 2. Center for Social Data Analytics (2019), 1\u201322."},{"key":"e_1_3_2_2_95_1","volume-title":"Latent class modeling with covariates: Two improved three-step approaches. Political analysis 18, 4","author":"Vermunt Jeroen K","year":"2010","unstructured":"Jeroen K Vermunt . 2010. Latent class modeling with covariates: Two improved three-step approaches. Political analysis 18, 4 ( 2010 ), 450\u2013469. Jeroen K Vermunt. 2010. Latent class modeling with covariates: Two improved three-step approaches. Political analysis 18, 4 (2010), 450\u2013469."},{"key":"e_1_3_2_2_96_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 ). 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)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_97_1","DOI":"10.1145\/3442188.3445915"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_98_1","DOI":"10.1145\/3397481.3450650"},{"key":"e_1_3_2_2_99_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1177\/0095798420930932","article-title":"Latent class analysis: a guide to best practice","volume":"46","author":"Weller Bridget E","year":"2020","unstructured":"Bridget E Weller , Natasha K Bowen , and Sarah J Faubert . 2020 . Latent class analysis: a guide to best practice . Journal of Black Psychology 46 , 4 (2020), 287 \u2013 311 . Bridget E Weller, Natasha K Bowen, and Sarah J Faubert. 2020. Latent class analysis: a guide to best practice. Journal of Black Psychology 46, 4 (2020), 287\u2013311.","journal-title":"Journal of Black Psychology"},{"key":"e_1_3_2_2_100_1","volume-title":"Learning to complement humans. arXiv preprint arXiv:2005.00582","author":"Wilder Bryan","year":"2020","unstructured":"Bryan Wilder , Eric Horvitz , and Ece Kamar . 2020. Learning to complement humans. arXiv preprint arXiv:2005.00582 ( 2020 ). Bryan Wilder, Eric Horvitz, and Ece Kamar. 2020. Learning to complement humans. arXiv preprint arXiv:2005.00582 (2020)."},{"key":"e_1_3_2_2_101_1","volume-title":"You Complete Me: Human-AI Teams and Complementary Expertise. In CHI Conference on Human Factors in Computing Systems. 1\u201328","author":"Zhang Qiaoning","year":"2022","unstructured":"Qiaoning Zhang , Matthew L Lee , and Scott Carter . 2022 . You Complete Me: Human-AI Teams and Complementary Expertise. In CHI Conference on Human Factors in Computing Systems. 1\u201328 . Qiaoning Zhang, Matthew L Lee, and Scott Carter. 2022. You Complete Me: Human-AI Teams and Complementary Expertise. In CHI Conference on Human Factors in Computing Systems. 1\u201328."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_102_1","DOI":"10.1145\/3351095.3372852"}],"event":{"acronym":"FAccT '23","name":"FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency","location":"Chicago IL USA"},"container-title":["2023 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594036","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3593013.3594036","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:48:04Z","timestamp":1750178884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3593013.3594036"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,12]]},"references-count":102,"alternative-id":["10.1145\/3593013.3594036","10.1145\/3593013"],"URL":"https:\/\/doi.org\/10.1145\/3593013.3594036","relation":{},"subject":[],"published":{"date-parts":[[2023,6,12]]},"assertion":[{"value":"2023-06-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}