{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:34Z","timestamp":1750220194884,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"POR Lisboa"},{"name":"Fundac\u00e3o para a Ci\u00eancia e a Tecnologia"},{"name":"POR Norte"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,26]]},"DOI":"10.1145\/3514094.3534156","type":"proceedings-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T22:25:13Z","timestamp":1658960713000},"page":"778-787","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainability's Gain is Optimality's Loss?"],"prefix":"10.1145","author":[{"given":"Charles","family":"Wan","sequence":"first","affiliation":[{"name":"Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo","family":"Belo","sequence":"additional","affiliation":[{"name":"Nova School of Business and Economics, Universidade Nova de Lisboa &amp; Rotterdam School of Management, Erasmus University, Carcavelos, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leid","family":"Zejnilovic","sequence":"additional","affiliation":[{"name":"Nova School of Business and Economics, Universidade Nova de Lisboa, Carcavelos, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.16.0163"},{"key":"e_1_3_2_2_2_1","volume-title":"Fairness in machine learning. Nips tutorial","author":"Barocas Solon","year":"2017","unstructured":"Solon Barocas , Moritz Hardt , and Arvind Narayanan . 2017. Fairness in machine learning. Nips tutorial , Vol. 1 ( 2017 ), 2. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2017. Fairness in machine learning. Nips tutorial, Vol. 1 (2017), 2."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_2_4_1","volume-title":"Science","volume":"358","author":"Brynjolfsson Erik","year":"2017","unstructured":"Erik Brynjolfsson and Tom Mitchell . 2017 . What can machine learning do? Workforce implications . Science , Vol. 358 , 6370 (2017), 1530--1534. Erik Brynjolfsson and Tom Mitchell. 2017. What can machine learning do? Workforce implications. Science, Vol. 358, 6370 (2017), 1530--1534."},{"key":"e_1_3_2_2_6_1","volume-title":"Conference on fairness, accountability and transparency. PMLR, 77--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. PMLR, 77--91 . Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77--91."},{"key":"e_1_3_2_2_7_1","volume-title":"The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. arXiv preprint arXiv:2009.11023","author":"Camburu Oana-Maria","year":"2020","unstructured":"Oana-Maria Camburu , Eleonora Giunchiglia , Jakob Foerster , Thomas Lukasiewicz , and Phil Blunsom . 2020. The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. arXiv preprint arXiv:2009.11023 ( 2020 ). Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster, Thomas Lukasiewicz, and Phil Blunsom. 2020. The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. arXiv preprint arXiv:2009.11023 (2020)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1037\/xge0000033"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2016.2643"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1071-5819(03)00038-7"},{"key":"e_1_3_2_2_12_1","unstructured":"European Commission. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95\/46\/EC (General Data Protection Regulation) (Text with EEA relevance). https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj  European Commission. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95\/46\/EC (General Data Protection Regulation) (Text with EEA relevance). https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj"},{"key":"e_1_3_2_2_13_1","volume-title":"From Noise to Bias: Overconfidence in New Product Forecasting. Management Science","author":"Feiler Daniel","year":"2021","unstructured":"Daniel Feiler and Jordan Tong . 2021. From Noise to Bias: Overconfidence in New Product Forecasting. Management Science ( 2021 ). Daniel Feiler and Jordan Tong. 2021. From Noise to Bias: Overconfidence in New Product Forecasting. Management Science (2021)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458455"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"e_1_3_2_2_17_1","unstructured":"T. Hastie R. Tibshirani and J.H. Friedman. 2009. The Elements of Statistical Learning: Data Mining Inference and Prediction. Springer. 2008941148 https:\/\/books.google.nl\/books?id=eBSgoAEACAAJ  T. Hastie R. Tibshirani and J.H. Friedman. 2009. The Elements of Statistical Learning: Data Mining Inference and Prediction. Springer. 2008941148 https:\/\/books.google.nl\/books?id=eBSgoAEACAAJ"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445886"},{"key":"e_1_3_2_2_19_1","volume-title":"When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business. Management Science","author":"Kawaguchi Kohei","year":"2020","unstructured":"Kohei Kawaguchi . 2020. When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business. Management Science ( 2020 ). Kohei Kawaguchi. 2020. When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business. Management Science (2020)."},{"key":"e_1_3_2_2_20_1","first-page":"1163","article-title":"Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination","volume":"113","author":"Kohler-Hausmann Issa","year":"2018","unstructured":"Issa Kohler-Hausmann . 2018 . Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination . Nw. UL Rev. , Vol. 113 (2018), 1163 . Issa Kohler-Hausmann. 2018. Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination. Nw. UL Rev., Vol. 113 (2018), 1163.","journal-title":"Nw. UL Rev."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Machine Learning. PMLR, 5491--5500","author":"Kumar I Elizabeth","year":"2020","unstructured":"I Elizabeth Kumar , Suresh Venkatasubramanian , Carlos Scheidegger , and Sorelle Friedler . 2020 . Problems with Shapley-value-based explanations as feature importance measures . In International Conference on Machine Learning. PMLR, 5491--5500 . I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler. 2020. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning. PMLR, 5491--5500."},{"volume-title":"Explaining the Evidence: How the Mind Investigates the World","author":"Lagnado David A.","key":"e_1_3_2_2_22_1","unstructured":"David A. Lagnado . 2021. Explaining the Evidence: How the Mind Investigates the World . Cambridge University Press . https:\/\/doi.org\/10.1017\/9780511794520 10.1017\/9780511794520 David A. Lagnado. 2021. Explaining the Evidence: How the Mind Investigates the World. Cambridge University Press. https:\/\/doi.org\/10.1017\/9780511794520"},{"key":"e_1_3_2_2_23_1","volume-title":"To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis","author":"Lebovitz Sarah","year":"2022","unstructured":"Sarah Lebovitz , Hila Lifshitz-Assaf , and Natalia Levina . 2022. To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis . Organization Science ( 2022 ). Sarah Lebovitz, Hila Lifshitz-Assaf, and Natalia Levina. 2022. To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science (2022)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2020.1229"},{"key":"e_1_3_2_2_25_1","volume-title":"Decision-Driven Regularization: A Blended Model for Predict-then-Optimize. Available at SSRN 3623006","author":"Loke Gar Goei","year":"2021","unstructured":"Gar Goei Loke , Qinshen Tang , and Yangge Xiao . 2021. Decision-Driven Regularization: A Blended Model for Predict-then-Optimize. Available at SSRN 3623006 ( 2021 ). Gar Goei Loke, Qinshen Tang, and Yangge Xiao. 2021. Decision-Driven Regularization: A Blended Model for Predict-then-Optimize. Available at SSRN 3623006 (2021)."},{"key":"e_1_3_2_2_26_1","volume-title":"Simplicity and probability in causal explanation. Cognitive psychology","author":"Lombrozo Tania","year":"2007","unstructured":"Tania Lombrozo . 2007. Simplicity and probability in causal explanation. Cognitive psychology , Vol. 55 , 3 ( 2007 ), 232--257. Tania Lombrozo. 2007. Simplicity and probability in causal explanation. Cognitive psychology, Vol. 55, 3 (2007), 232--257."},{"key":"e_1_3_2_2_27_1","volume-title":"Bayesian generic priors for causal learning. Psychological review","author":"Lu Hongjing","year":"2008","unstructured":"Hongjing Lu , Alan L Yuille , Mimi Liljeholm , Patricia W Cheng , and Keith J Holyoak . 2008. Bayesian generic priors for causal learning. Psychological review , Vol. 115 , 4 ( 2008 ), 955. Hongjing Lu, Alan L Yuille, Mimi Liljeholm, Patricia W Cheng, and Keith J Holyoak. 2008. Bayesian generic priors for causal learning. Psychological review, Vol. 115, 4 (2008), 955."},{"key":"e_1_3_2_2_28_1","volume-title":"From local explanations to global understanding with explainable AI for trees. Nature machine intelligence","author":"Lundberg Scott M","year":"2020","unstructured":"Scott M Lundberg , Gabriel Erion , Hugh Chen , Alex DeGrave , Jordan M Prutkin , Bala Nair , Ronit Katz , Jonathan Himmelfarb , Nisha Bansal , and Su-In Lee . 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence , Vol. 2 , 1 ( 2020 ), 56--67. Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, Vol. 2, 1 (2020), 56--67."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_2_30_1","unstructured":"Christoph Molnar. 2020. Interpretable machine learning.Lulu.com.  Christoph Molnar. 2020. Interpretable machine learning.Lulu.com."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"e_1_3_2_2_32_1","volume-title":"Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology","author":"Nickerson Raymond S","year":"1998","unstructured":"Raymond S Nickerson . 1998. Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology , Vol. 2 , 2 ( 1998 ), 175--220. Raymond S Nickerson. 1998. Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology, Vol. 2, 2 (1998), 175--220."},{"key":"e_1_3_2_2_33_1","unstructured":"Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books.  Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287569"},{"volume-title":"Administrative behavior","author":"Simon Herbert A","key":"e_1_3_2_2_36_1","unstructured":"Herbert A Simon . 2013. Administrative behavior . Simon and Schuster . Herbert A Simon. 2013. Administrative behavior. Simon and Schuster."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2020.3678"},{"key":"e_1_3_2_2_38_1","volume-title":"The impact of explanation facilities on user acceptance of expert systems advice. Mis Quarterly","author":"Richard Ye L","year":"1995","unstructured":"L Richard Ye and Paul E Johnson . 1995. The impact of explanation facilities on user acceptance of expert systems advice. Mis Quarterly ( 1995 ), 157--172. L Richard Ye and Paul E Johnson. 1995. The impact of explanation facilities on user acceptance of expert systems advice. Mis Quarterly (1995), 157--172."}],"event":{"name":"AIES '22: AAAI\/ACM Conference on AI, Ethics, and Society","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","AAAI"],"location":"Oxford United Kingdom","acronym":"AIES '22"},"container-title":["Proceedings of the 2022 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534156","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514094.3534156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:36Z","timestamp":1750186956000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514094.3534156"}},"subtitle":["How Explanations Bias Decision-making"],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":37,"alternative-id":["10.1145\/3514094.3534156","10.1145\/3514094"],"URL":"https:\/\/doi.org\/10.1145\/3514094.3534156","relation":{},"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"2022-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}