{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:09:46Z","timestamp":1750748986199,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Facebook Research award","award":["2020 Probability and Programming Research Award"],"award-info":[{"award-number":["2020 Probability and Programming Research Award"]}]},{"name":"University of Padova","award":["SID2018 project Analysis of STatic Analyses (ASTA)"],"award-info":[{"award-number":["SID2018 project Analysis of STatic Analyses (ASTA)"]}]},{"name":"Italian Ministry of University and Research","award":["PRIN2017 project no. 201784YSZ5 AnalysiS of PRogram Analyses (ASPRA)"],"award-info":[{"award-number":["PRIN2017 project no. 201784YSZ5 AnalysiS of PRogram Analyses (ASPRA)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482342","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:31:16Z","timestamp":1636990276000},"page":"1508-1517","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Fairness-Aware Training of Decision Trees by Abstract Interpretation"],"prefix":"10.1145","author":[{"given":"Francesco","family":"Ranzato","sequence":"first","affiliation":[{"name":"University of Padova, Padova, Italy"}]},{"given":"Caterina","family":"Urban","sequence":"additional","affiliation":[{"name":"INRIA and Ecole Normale Superieure & Universite' PSL, Paris, France"}]},{"given":"Marco","family":"Zanella","sequence":"additional","affiliation":[{"name":"University of Padova, Padova, Italy"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011418"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455453"},{"key":"e_1_3_2_1_3_1","volume-title":"ProPublica","volume":"23","author":"Angwin Julia","year":"2016","unstructured":"Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016 . Machine Bias . ProPublica , May , Vol. 23 (2016), 2016. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias. ProPublica, May, Vol. 23 (2016), 2016. https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"e_1_3_2_1_4_1","first-page":"671","article-title":"Big Data's Disparate Impact","volume":"104","author":"Barocas Solon","year":"2016","unstructured":"Solon Barocas and Andrew D. Selbst . 2016 . Big Data's Disparate Impact . California Law Review , Vol. 104 (2016), 671 . https:\/\/doi.org\/10.15779\/Z38BG31 10.15779\/Z38BG31 Solon Barocas and Andrew D. Selbst. 2016. Big Data's Disparate Impact. California Law Review, Vol. 104 (2016), 671. https:\/\/doi.org\/10.15779\/Z38BG31","journal-title":"California Law Review"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5633-9"},{"volume-title":"Avoiding Overfitting of Decision Trees","author":"Bramer Max","key":"e_1_3_2_1_6_1","unstructured":"Max Bramer . 2013. Avoiding Overfitting of Decision Trees . Springer , 121--136. https:\/\/doi.org\/10.1007\/978-1-4471-4884--5_9 10.1007\/978-1-4471-4884--5_9 Max Bramer. 2013. Avoiding Overfitting of Decision Trees. Springer, 121--136. https:\/\/doi.org\/10.1007\/978-1-4471-4884--5_9"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_8_1","unstructured":"Leo Breiman J. H. Friedman R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth.  Leo Breiman J. H. Friedman R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth."},{"key":"e_1_3_2_1_9_1","volume-title":"Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency (FAT 2018)","volume":"91","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru . 2018 . Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency (FAT 2018) (Proceedings of Machine Learning Research , Vol. 81). PMLR, 77-- 91 . http:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency (FAT 2018) (Proceedings of Machine Learning Research, Vol. 81). PMLR, 77--91. http:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358149"},{"key":"e_1_3_2_1_11_1","volume-title":"Seyum Assefa Abebe, and Salvatore Orlando","author":"Calzavara Stefano","year":"2020","unstructured":"Stefano Calzavara , Claudio Lucchese , Gabriele Tolomei , Seyum Assefa Abebe, and Salvatore Orlando . 2020 . TREANT: training evasion-aware decision trees. Data Mining and Knowledge Discovery ( 2020). https:\/\/doi.org\/10.1007\/s10618-020-00694--9 10.1007\/s10618-020-00694--9 Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, Seyum Assefa Abebe, and Salvatore Orlando. 2020. TREANT: training evasion-aware decision trees. Data Mining and Knowledge Discovery (2020). https:\/\/doi.org\/10.1007\/s10618-020-00694--9"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_13_1","volume-title":"Proc. 36th Int. Conf. on Machine Learning, (ICML","author":"Chen Hongge","year":"2019","unstructured":"Hongge Chen , Huan Zhang , Duane S. Boning , and Cho-Jui Hsieh . 2019 . Robust Decision Trees Against Adversarial Examples . In Proc. 36th Int. Conf. on Machine Learning, (ICML 2019). 1122--1131. http:\/\/proceedings.mlr.press\/v97\/chen19m.html Hongge Chen, Huan Zhang, Duane S. Boning, and Cho-Jui Hsieh. 2019. Robust Decision Trees Against Adversarial Examples. In Proc. 36th Int. Conf. on Machine Learning, (ICML 2019). 1122--1131. http:\/\/proceedings.mlr.press\/v97\/chen19m.html"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2016.0047"},{"volume-title":"Principles of Abstract Interpretation","author":"Cousot Patrick","key":"e_1_3_2_1_15_1","unstructured":"Patrick Cousot . 2021. Principles of Abstract Interpretation . MIT Press . Patrick Cousot. 2021. Principles of Abstract Interpretation. MIT Press."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/512950.512973"},{"key":"e_1_3_2_1_17_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository.  Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_1_19_1","unstructured":"European Commission. 2021. Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act). https:\/\/digital-strategy.ec.europa.eu\/en\/library\/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence.  European Commission. 2021. Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act). https:\/\/digital-strategy.ec.europa.eu\/en\/library\/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence-artificial-intelligence."},{"key":"e_1_3_2_1_20_1","volume-title":"Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics","author":"Friedman Jerome H","year":"2001","unstructured":"Jerome H Friedman . 2001. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics ( 2001 ), 1189--1232. https:\/\/www.jstor.org\/stable\/2699986 Jerome H Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics (2001), 1189--1232. https:\/\/www.jstor.org\/stable\/2699986"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106277"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134599"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00124-2"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157469"},{"volume-title":"Adaptive Control of Ill-Defined Systems","author":"Holland John H","key":"e_1_3_2_1_25_1","unstructured":"John H Holland . 1984. Genetic algorithms and adaptation . In Adaptive Control of Ill-Defined Systems . Springer , 317--333. https:\/\/doi.org\/10.1007\/978-1-4684-8941-5_21 10.1007\/978-1-4684-8941-5_21 John H Holland. 1984. Genetic algorithms and adaptation. In Adaptive Control of Ill-Defined Systems. Springer, 317--333. https:\/\/doi.org\/10.1007\/978-1-4684-8941-5_21"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045642"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/645527.657457"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbankfin.2010.06.001"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1561\/2400000035"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1569901.1570016"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aax2342"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278742"},{"key":"e_1_3_2_1_35_1","volume-title":"FATT: Fairness Aware Tree Training. https:\/\/github.com\/fatt21\/fatt.","author":"Ranzato Francesco","year":"2021","unstructured":"Francesco Ranzato , Caterina Urban , and Marco Zanella . 2021 . FATT: Fairness Aware Tree Training. https:\/\/github.com\/fatt21\/fatt. Francesco Ranzato, Caterina Urban, and Marco Zanella. 2021. FATT: Fairness Aware Tree Training. https:\/\/github.com\/fatt21\/fatt."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5998"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3449639.3459286"},{"volume-title":"Introduction to Static Analysis: An Abstract Interpretation Perspective","author":"Rival Xavier","key":"e_1_3_2_1_38_1","unstructured":"Xavier Rival and Kwangkeun Yi. 2020. Introduction to Static Analysis: An Abstract Interpretation Perspective . The MIT Press . Xavier Rival and Kwangkeun Yi. 2020. Introduction to Static Analysis: An Abstract Interpretation Perspective. The MIT Press."},{"key":"e_1_3_2_1_39_1","volume-title":"Proc. of the 37th Int. Conf. on Machine Learning (ICML 2020)","volume":"8157","author":"Roh Yuji","year":"2020","unstructured":"Yuji Roh , Kangwook Lee , Steven Whang , and Changho Suh . 2020 . FR-Train: A Mutual Information-Based Approach to Fair and Robust Training . In Proc. of the 37th Int. Conf. on Machine Learning (ICML 2020) (Proceedings of Machine Learning Research , Vol. 119). PMLR, 8147-- 8157 . http:\/\/proceedings.mlr.press\/v119\/roh20a.html Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh. 2020. FR-Train: A Mutual Information-Based Approach to Fair and Robust Training. In Proc. of the 37th Int. Conf. on Machine Learning (ICML 2020) (Proceedings of Machine Learning Research, Vol. 119). PMLR, 8147--8157. http:\/\/proceedings.mlr.press\/v119\/roh20a.html"},{"key":"e_1_3_2_1_40_1","volume-title":"Learning Certified Individually Fair Representations. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems (NeurIPS 2020","author":"Ruoss Anian","year":"2020","unstructured":"Anian Ruoss , Mislav Balunovic , Marc Fischer , and Martin T. Vechev . 2020 . Learning Certified Individually Fair Representations. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems (NeurIPS 2020 ). https:\/\/proceedings.neurips.cc\/paper\/ 2020 \/hash\/55d491cf951b1b920900684d71419282-Abstract.html Anian Ruoss, Mislav Balunovic, Marc Fischer, and Martin T. Vechev. 2020. Learning Certified Individually Fair Representations. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems (NeurIPS 2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55d491cf951b1b920900684d71419282-Abstract.html"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/3398761.3398960"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.294849"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428253"},{"key":"e_1_3_2_1_44_1","volume-title":"A Review of Formal Methods applied to Machine Learning. CoRR","author":"Urban Caterina","year":"2021","unstructured":"Caterina Urban and Antoine Min\u00e9 . 2021. A Review of Formal Methods applied to Machine Learning. CoRR , Vol. abs\/ 2104 .02466 ( 2021 ). https:\/\/arxiv.org\/abs\/2104.02466 Caterina Urban and Antoine Min\u00e9. 2021. A Review of Formal Methods applied to Machine Learning. CoRR, Vol. abs\/2104.02466 (2021). https:\/\/arxiv.org\/abs\/2104.02466"},{"key":"e_1_3_2_1_45_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Yurochkin Mikhail","year":"2020","unstructured":"Mikhail Yurochkin , Amanda Bower , and Yuekai Sun . 2020 . Training individually fair ML models with sensitive subspace robustness . In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia , April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=B1gdkxHFDH Mikhail Yurochkin, Amanda Bower, and Yuekai Sun. 2020. Training individually fair ML models with sensitive subspace robustness. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=B1gdkxHFDH"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052660"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Queensland Australia","acronym":"CIKM '21"},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482342","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:22Z","timestamp":1750191142000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":46,"alternative-id":["10.1145\/3459637.3482342","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482342","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}