{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T21:19:48Z","timestamp":1773695988189,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1845487"],"award-info":[{"award-number":["1845487"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,3]]},"DOI":"10.1145\/3442188.3445865","type":"proceedings-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T01:45:48Z","timestamp":1614217548000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Fairness Violations and Mitigation under Covariate Shift"],"prefix":"10.1145","author":[{"given":"Harvineet","family":"Singh","sequence":"first","affiliation":[{"name":"Center for Data Science, New York University, New York City, NY, USA"}]},{"given":"Rina","family":"Singh","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York City, NY, USA"}]},{"given":"Vishwali","family":"Mhasawade","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York City, NY, USA"}]},{"given":"Rumi","family":"Chunara","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering; School of Global Public Health, New York University, New York City, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,3]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International Conference on Machine Learning. 60--69","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018. A Reductions Approach to Fair Classification. In International Conference on Machine Learning. 60--69."},{"key":"e_1_3_2_2_2_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)."},{"key":"e_1_3_2_2_3_1","volume-title":"A theory of learning from different domains. Machine learning 79, 1-2","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79, 1-2 (2010), 151--175."},{"key":"e_1_3_2_2_4_1","volume-title":"International Conference on Artificial Intelligence and Statistics. 129--136","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, Tyler Lu, Teresa Luu, and D\u00e1vid P\u00e1l. 2010. Impossibility theorems for domain adaptation. In International Conference on Artificial Intelligence and Statistics. 129--136."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273507"},{"key":"e_1_3_2_2_6_1","volume-title":"1st Symposium on Foundations of Responsible Computing (FORC","author":"Blum Avrim","year":"2020","unstructured":"Avrim Blum and Kevin Stangl. 2020. Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Schloss Dagstuhl-Leibniz-Zentrum f\u00fcr Informatik."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2017.2"},{"key":"e_1_3_2_2_9_1","volume-title":"Ethical Machine Learning in Health Care. To appear in Annual Review of Biomedical Data Science","author":"Chen Irene Y.","year":"2021","unstructured":"Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. 2021. Ethical Machine Learning in Health Care. To appear in Annual Review of Biomedical Data Science (2021). arXiv:2009.10576"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017801"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314236"},{"key":"e_1_3_2_2_13_1","volume-title":"Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. In International Conference on Machine Learning. 1397--1405","author":"Cotter Andrew","year":"2019","unstructured":"Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, and Seungil You. 2019. Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. In International Conference on Machine Learning. 1397--1405."},{"key":"e_1_3_2_2_14_1","volume-title":"Optimal transport for domain adaptation","author":"Courty Nicolas","year":"2016","unstructured":"Nicolas Courty, R\u00e9mi Flamary, Devis Tuia, and Alain Rakotomamonjy. 2016. Optimal transport for domain adaptation. IEEE transactions on pattern analysis and machine intelligence 39, 9 (2016), 1853--1865."},{"key":"e_1_3_2_2_15_1","unstructured":"Michele Donini Luca Oneto Shai Ben-David John S Shawe-Taylor and Massimiliano Pontil. 2018. Empirical risk minimization under fairness constraints. In Advances in Neural Information Processing Systems. 2791--2801."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.ackd.2008.04.003"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287589"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2013080867"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2015.02.337"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2840728.2840730"},{"key":"e_1_3_2_2_22_1","unstructured":"Moritz Hardt Eric Price Nati Srebro et al. 2016. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315--3323."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02985802"},{"key":"e_1_3_2_2_24_1","volume-title":"Seminars in nephrology","author":"Hodgson Luke E","unstructured":"Luke E Hodgson, Nicholas Selby, Tao-Min Huang, and Lui G Forni. 2019. The role of risk prediction models in prevention and management of AKI. In Seminars in nephrology, Vol. 39. Elsevier, 421--430."},{"key":"e_1_3_2_2_25_1","volume-title":"Stable and Fair Classification. In International Conference on Machine Learning. 2879--2890","author":"Huang Lingxiao","year":"2019","unstructured":"Lingxiao Huang and Nisheeth Vishnoi. 2019. Stable and Fair Classification. In International Conference on Machine Learning. 2879--2890."},{"key":"e_1_3_2_2_26_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data 3 (2016), 160035."},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning.","author":"Kallus Nathan","year":"2018","unstructured":"Nathan Kallus and Angela Zhou. 2018. Residual Unfairness in Fair Machine Learning from Prejudiced Data. In Proceedings of the 35th International Conference on Machine Learning."},{"key":"e_1_3_2_2_28_1","volume-title":"Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Sch\u00f6lkopf.","author":"Kilbertus Niki","year":"2017","unstructured":"Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Sch\u00f6lkopf. 2017. Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems. 656--666."},{"key":"e_1_3_2_2_29_1","unstructured":"Matt J Kusner Joshua Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual fairness. In Advances in Neural Information Processing Systems. 4066--4076."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/862"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1740-9713.2016.00960.x"},{"key":"e_1_3_2_2_32_1","volume-title":"Learning Adversarially Fair and Transferable Representations. In International Conference on Machine Learning. 3381--3390","author":"Madras David","year":"2018","unstructured":"David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018. Learning Adversarially Fair and Transferable Representations. In International Conference on Machine Learning. 3381--3390."},{"key":"e_1_3_2_2_33_1","unstructured":"Sara Magliacane Thijs van Ommen Tom Claassen Stephan Bongers Philip Versteeg and Joris M Mooij. 2018. Domain adaptation by using causal inference to predict invariant conditional distributions. In Advances in Neural Information Processing Systems. 10846--10856."},{"key":"e_1_3_2_2_34_1","volume-title":"Ensuring fairness beyond the training data. Advances in neural information processing systems","author":"Mandal Debmalya","year":"2020","unstructured":"Debmalya Mandal, Samuel Deng, Suman Jana, and Daniel Hsu. 2020. Ensuring fairness beyond the training data. Advances in neural information processing systems (2020)."},{"key":"e_1_3_2_2_35_1","volume-title":"Tenth International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 214--221","author":"Markowetz Florian","year":"2005","unstructured":"Florian Markowetz, Steffen Grossmann, and Rainer Spang. 2005. Probabilistic soft interventions in conditional Gaussian networks. In Tenth International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 214--221."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368555.3384451"},{"key":"e_1_3_2_2_37_1","volume-title":"Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867","author":"Mitchell Shira","year":"2018","unstructured":"Shira Mitchell, Eric Potash, Solon Barocas, Alexander D'Amour, and Kristian Lum. 2018. Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions. arXiv preprint arXiv:1811.07867 (2018)."},{"key":"e_1_3_2_2_38_1","first-page":"1","article-title":"Joint Causal Inference from Multiple Contexts","volume":"21","author":"Mooij Joris M.","year":"2020","unstructured":"Joris M. Mooij, Sara Magliacane, and Tom Claassen. 2020. Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research 21, 99 (2020), 1--108. http:\/\/jmlr.org\/papers\/v21\/17- 123.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11553"},{"key":"e_1_3_2_2_40_1","volume-title":"Proc. Conf. Fairness Accountability Transp.","volume":"1170","author":"Narayanan Arvind","year":"2018","unstructured":"Arvind Narayanan. 2018. Translation tutorial: 21 fairness definitions and their politics. In Proc. Conf. Fairness Accountability Transp., New York, USA, Vol. 1170."},{"key":"e_1_3_2_2_41_1","volume-title":"Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. arXiv preprint arXiv:1908.00690","author":"Nestor Bret","year":"2019","unstructured":"Bret Nestor, Matthew McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes, Anna Goldenberg, and Marzyeh Ghassemi. 2019. Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. arXiv preprint arXiv:1908.00690 (2019)."},{"key":"e_1_3_2_2_42_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--453."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.17226\/12875"},{"key":"e_1_3_2_2_44_1","volume-title":"Learning fair and transferable representations. arXiv preprint arXiv:1906.10673","author":"Oneto Luca","year":"2019","unstructured":"Luca Oneto, Michele Donini, Andreas Maurer, and Massimiliano Pontil. 2019. Learning fair and transferable representations. arXiv preprint arXiv:1906.10673 (2019)."},{"key":"e_1_3_2_2_45_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.169"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12167"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314278"},{"key":"e_1_3_2_2_49_1","volume-title":"Counterfactual Reasoning for Fair Clinical Risk Prediction. In Machine Learning for Healthcare Conference. 325--358","author":"Pfohl Stephen R","year":"2019","unstructured":"Stephen R Pfohl, Tony Duan, Daisy Yi Ding, and Nigam H Shah. 2019. Counterfactual Reasoning for Fair Clinical Risk Prediction. In Machine Learning for Healthcare Conference. 325--358."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-soc-073014-112305"},{"key":"e_1_3_2_2_51_1","volume-title":"Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification. arXiv preprint arXiv:1909.01940","author":"Pooch Eduardo HP","year":"2019","unstructured":"Eduardo HP Pooch, Pedro L Ballester, and Rodrigo C Barros. 2019. Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification. arXiv preprint arXiv:1909.01940 (2019)."},{"key":"e_1_3_2_2_52_1","volume-title":"Dataset shift in machine learning","author":"Quionero-Candela Joaquin","unstructured":"Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2009. Dataset shift in machine learning. The MIT Press."},{"key":"e_1_3_2_2_53_1","volume-title":"Robust Fairness under Covariate Shift. arXiv preprint arXiv:2010.05166","author":"Rezaei Ashkan","year":"2020","unstructured":"Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian Ziebart. 2020. Robust Fairness under Covariate Shift. arXiv preprint arXiv:2010.05166 (2020)."},{"key":"e_1_3_2_2_54_1","volume-title":"Thomas PA Debray, Doug G Altman, Karel GM Moons, and Gary S Collins.","author":"Riley Richard D","year":"2016","unstructured":"Richard D Riley, Joie Ensor, Kym IE Snell, Thomas PA Debray, Doug G Altman, Karel GM Moons, and Gary S Collins. 2016. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. bmj 353 (2016), i3140."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.5555\/3291125.3291161"},{"key":"e_1_3_2_2_56_1","volume-title":"Anchor regression: heterogeneous data meets causality. arXiv preprint arXiv:1801.06229","author":"Rothenh\u00e4usler Dominik","year":"2018","unstructured":"Dominik Rothenh\u00e4usler, Nicolai Meinshausen, Peter B\u00fchlmann, and Jonas Peters. 2018. Anchor regression: heterogeneous data meets causality. arXiv preprint arXiv:1801.06229 (2018)."},{"key":"e_1_3_2_2_57_1","volume-title":"H Chi","author":"Schumann Candice","year":"2019","unstructured":"Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, and Ed H Chi. 2019. Transfer of Machine Learning Fairness across Domains. arXiv preprint arXiv:1906.09688 (2019)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(00)00115-4"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372839"},{"key":"e_1_3_2_2_60_1","volume-title":"Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms.. In UAI. 947--957.","author":"Subbaswamy Adarsh","year":"2018","unstructured":"Adarsh Subbaswamy and Suchi Saria. 2018. Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms.. In UAI. 947--957."},{"key":"e_1_3_2_2_61_1","volume-title":"Shift-Stable Models. arXiv preprint arXiv:2002.08948","author":"Subbaswamy Adarsh","year":"2020","unstructured":"Adarsh Subbaswamy and Suchi Saria. 2020. I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models. arXiv preprint arXiv:2002.08948 (2020)."},{"key":"e_1_3_2_2_62_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. 3118--3127","author":"Subbaswamy Adarsh","year":"2019","unstructured":"Adarsh Subbaswamy, Peter Schulam, and Suchi Saria. 2019. Preventing failures due to dataset shift: Learning predictive models that transport. In The 22nd International Conference on Artificial Intelligence and Statistics. 3118--3127."},{"key":"e_1_3_2_2_63_1","unstructured":"Masashi Sugiyama Shinichi Nakajima Hisashi Kashima Paul V Buenau and Motoaki Kawanabe. 2008. Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in neural information processing systems. 1433--1440."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"crossref","unstructured":"Nenad Toma\u0161ev Xavier Glorot Jack W Rae Michal Zielinski Harry Askham Andre Saraiva Anne Mottram Clemens Meyer Suman Ravuri Ivan Protsyuk et al. 2019. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572 7767 (2019) 116.","DOI":"10.1038\/s41586-019-1390-1"},{"key":"e_1_3_2_2_65_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"6382","author":"Ustun Berk","year":"2019","unstructured":"Berk Ustun, Yang Liu, and David Parkes. 2019. Fairness without Harm: Decoupled Classifiers with Preference Guarantees. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol.97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, California, USA, 6373--6382. http:\/\/proceedings.mlr.press\/v97\/ustun19a.html"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002162"},{"key":"e_1_3_2_2_67_1","first-page":"1","article-title":"Fairness Constraints: A Flexible Approach for Fair Classification","volume":"20","author":"Zafar Muhammad Bilal","year":"2019","unstructured":"Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi. 2019. Fairness Constraints: A Flexible Approach for Fair Classification. Journal of Machine Learning Research 20, 75 (2019), 1--42.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_68_1","volume-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS medicine 15, 11","author":"Zech John R","year":"2018","unstructured":"John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann. 2018. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS medicine 15, 11 (2018), e1002683."},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13206"}],"event":{"name":"FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency","location":"Virtual Event Canada","acronym":"FAccT '21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445865","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445865","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442188.3445865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:56Z","timestamp":1750193336000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442188.3445865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":69,"alternative-id":["10.1145\/3442188.3445865","10.1145\/3442188"],"URL":"https:\/\/doi.org\/10.1145\/3442188.3445865","relation":{},"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"2021-03-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}