{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:39:52Z","timestamp":1771699192749,"version":"3.50.1"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006112","name":"Microsoft Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006112","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10618-022-00910-8","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T08:02:49Z","timestamp":1674460969000},"page":"1858-1884","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Social norm bias: residual harms of fairness-aware algorithms"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-2929","authenticated-orcid":false,"given":"Myra","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Maria","family":"De-Arteaga","sequence":"additional","affiliation":[]},{"given":"Lester","family":"Mackey","sequence":"additional","affiliation":[]},{"given":"Adam Tauman","family":"Kalai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"910_CR1","unstructured":"Adi Y, Kermany E, Belinkov Y, Lavi O, Goldberg Y (2017) Fine-grained analysis of sentence embeddings using auxiliary prediction tasks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, https:\/\/openreview.net\/forum?id=BJh6Ztuxl"},{"key":"910_CR2","unstructured":"Agarwal A, Beygelzimer A, Dud\u00edk M, Langford J, Wallach H (2018) A reductions approach to fair classification. In: International conference on machine learning, PMLR, pp 60\u201369"},{"key":"910_CR3","unstructured":"Agius S, Tobler C (2012) Trans and intersex people. Discrimination on the grounds of sex, gender identity and gender expression. Office for Official Publications of the European Union"},{"key":"910_CR4","doi-asserted-by":"publisher","unstructured":"Antoniak M, Mimno D (2021) Bad seeds: evaluating lexical methods for bias measurement. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, pp 1889\u20131904, https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.148","DOI":"10.18653\/v1\/2021.acl-long.148"},{"key":"910_CR5","unstructured":"Bartl M, Nissim M, Gatt A (2020) Unmasking contextual stereotypes: Measuring and mitigating bert\u2019s gender bias. In: Proceedings of the second workshop on gender bias in natural language processing, pp 1\u201316"},{"issue":"4\/5","key":"910_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2019.2942287","volume":"63","author":"RK Bellamy","year":"2019","unstructured":"Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, Martino J, Mehta S, Mojsilovi\u0107 A et al (2019) Ai fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J Res Dev 63(4\/5):1\u20134","journal-title":"IBM J Res Dev"},{"issue":"1","key":"910_CR7","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol) 57(1):289\u2013300","journal-title":"J R Stat Soc: Ser B (Methodol)"},{"issue":"4","key":"910_CR8","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1257\/0002828042002561","volume":"94","author":"M Bertrand","year":"2004","unstructured":"Bertrand M, Mullainathan S (2004) Are Emily and Greg more employable than Lakisha and Jamal? a field experiment on labor market discrimination. Am Econ Rev 94(4):991\u20131013","journal-title":"Am Econ Rev"},{"key":"910_CR9","unstructured":"Bird S, Dud\u00edk M, Edgar R, Horn B, Lutz R, Milan V, Sameki M, Wallach H, Walker K (2020) Fairlearn: a toolkit for assessing and improving fairness in AI. Tech. Rep. MSR-TR-2020-32, Microsoft, https:\/\/www.microsoft.com\/en-us\/research\/publication\/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai\/"},{"key":"910_CR10","doi-asserted-by":"crossref","unstructured":"Blodgett SL, Barocas S, Daum\u00e9 III H, Wallach H (2020) Language (technology) is power: a critical survey of \u201cbias\u201d in nlp. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5454\u20135476","DOI":"10.18653\/v1\/2020.acl-main.485"},{"key":"910_CR11","doi-asserted-by":"publisher","unstructured":"Blodgett SL, Lopez G, Olteanu A, Sim R, Wallach H (2021) Stereotyping Norwegian salmon: an inventory of pitfalls in fairness benchmark datasets. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, pp 1004\u20131015, https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.81","DOI":"10.18653\/v1\/2021.acl-long.81"},{"key":"910_CR12","unstructured":"Bogen M, Rieke A (2018) Help wanted: an examination of hiring algorithms, equity, and bias. Upturn, December 7"},{"key":"910_CR13","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135\u2013146","journal-title":"Trans Assoc Comput Linguist"},{"key":"910_CR14","first-page":"4349","volume":"29","author":"T Bolukbasi","year":"2016","unstructured":"Bolukbasi T, Chang KW, Zou JY, Saligrama V, Kalai AT (2016) Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Adv Neural Inf Process Syst 29:4349\u20134357","journal-title":"Adv Neural Inf Process Syst"},{"key":"910_CR15","doi-asserted-by":"publisher","unstructured":"Bordia S, Bowman SR (2019) Identifying and reducing gender bias in word-level language models. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: student research workshop, association for computational linguistics, Minneapolis, Minnesota, pp 7\u201315, https:\/\/doi.org\/10.18653\/v1\/N19-3002","DOI":"10.18653\/v1\/N19-3002"},{"key":"910_CR16","unstructured":"Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. In: Friedler SA, Wilson C (eds) Conference on fairness, accountability and transparency, FAT 2018, 23-24 February 2018, New York, NY, USA, PMLR, proceedings of machine learning research, vol 81, pp 77\u201391, http:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html"},{"key":"910_CR17","volume-title":"Gender trouble: feminism and the subversion of identity","author":"J Butler","year":"1989","unstructured":"Butler J (1989) Gender trouble: feminism and the subversion of identity. Routledge, London"},{"issue":"6334","key":"910_CR18","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1126\/science.aal4230","volume":"356","author":"A Caliskan","year":"2017","unstructured":"Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183\u2013186","journal-title":"Science"},{"key":"910_CR19","unstructured":"Calmon FP, Wei D, Vinzamuri B, Ramamurthy KN, Varshney KR (2017) Optimized pre-processing for discrimination prevention. In: Proceedings of the 31st international conference on neural information processing systems, pp 3995\u20134004"},{"key":"910_CR20","doi-asserted-by":"crossref","unstructured":"Cao YT, III HD (2019) Toward gender-inclusive coreference resolution. CoRR, arXiv:1910.13913","DOI":"10.18653\/v1\/2020.acl-main.418"},{"issue":"71","key":"910_CR21","first-page":"425","volume":"24","author":"A Ceren","year":"2021","unstructured":"Ceren A, Tekir S (2021) Gender bias in occupation classification from the new york times obituaries. Dokuz Eyl\u00fcl \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi Fen ve M\u00fchendislik Dergisi 24(71):425\u2013436","journal-title":"Dokuz Eyl\u00fcl \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi Fen ve M\u00fchendislik Dergisi"},{"key":"910_CR22","unstructured":"Commission OHR (2021) Gender identity and gender expression. http:\/\/www.ohrc.on.ca\/en\/policy-preventing-discrimination-because-gender-identity-and-gender-expression\/3-gender-identity-and-gender-expression"},{"issue":"3","key":"910_CR23","doi-asserted-by":"publisher","first-page":"444","DOI":"10.3758\/bf03195592","volume":"36","author":"JT Crawford","year":"2004","unstructured":"Crawford JT, Leynes PA, Mayhorn CB, Bink ML (2004) Champagne, beer, or coffee? a corpus of gender-related and neutral words. Behav Res Methods Instrum Comput 36(3):444\u2013458. https:\/\/doi.org\/10.3758\/bf03195592","journal-title":"Behav Res Methods Instrum Comput"},{"key":"910_CR24","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.2307\/1229039","volume":"43","author":"K Crenshaw","year":"1990","unstructured":"Crenshaw K (1990) Mapping the margins: intersectionality, identity politics, and violence against women of color. Stan L Rev 43:1241","journal-title":"Stan L Rev"},{"key":"910_CR25","doi-asserted-by":"crossref","unstructured":"Cryan J, Tang S, Zhang X, Metzger M, Zheng H, Zhao BY (2020) Detecting gender stereotypes: Lexicon versus supervised learning methods. In: Proceedings of the 2020 CHI conference on human factors in computing systems, pp 1\u201311","DOI":"10.1145\/3313831.3376488"},{"key":"910_CR26","doi-asserted-by":"crossref","unstructured":"De-Arteaga M, Romanov A, Wallach H, Chayes J, Borgs C, Chouldechova A, Geyik S, Kenthapadi K, Kalai AT (2019) Bias in bios: a case study of semantic representation bias in a high-stakes setting. In: Proceedings of the conference on fairness, accountability, and transparency, pp 120\u2013128","DOI":"10.1145\/3287560.3287572"},{"key":"910_CR27","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics, pp 4171\u20134186, https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"910_CR28","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"key":"910_CR29","unstructured":"Dwork C, Immorlica N, Kalai AT, Leiserson M (2018) Decoupled classifiers for group-fair and efficient machine learning. In: Conference on fairness, accountability and transparency, PMLR, pp 119\u2013133"},{"issue":"1","key":"910_CR30","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1086\/682955","volume":"30","author":"N Ensmenger","year":"2015","unstructured":"Ensmenger N (2015) beards, sandals, and other signs of rugged individualism: masculine culture within the computing professions. Osiris 30(1):38\u201365","journal-title":"Osiris"},{"issue":"16","key":"910_CR31","doi-asserted-by":"publisher","first-page":"E3635","DOI":"10.1073\/pnas.1720347115","volume":"115","author":"N Garg","year":"2018","unstructured":"Garg N, Schiebinger L, Jurafsky D, Zou J (2018) Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc Natl Acad Sci 115(16):E3635\u2013E3644","journal-title":"Proc Natl Acad Sci"},{"key":"910_CR32","doi-asserted-by":"crossref","unstructured":"Geyik SC, Ambler S, Kenthapadi K (2019) Fairness-aware ranking in search and recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2221\u20132231","DOI":"10.1145\/3292500.3330691"},{"issue":"12","key":"910_CR33","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1080\/13691058.2018.1437220","volume":"20","author":"JL Glick","year":"2018","unstructured":"Glick JL, Theall K, Andrinopoulos K, Kendall C (2018) For data\u2019s sake: dilemmas in the measurement of gender minorities. Cult Health Sex 20(12):1362\u20131377","journal-title":"Cult Health Sex"},{"key":"910_CR34","doi-asserted-by":"publisher","unstructured":"Gonen H, Goldberg Y (2019) Lipstick on a pig: debiasing methods cover up systematic gender biases in word embeddings but do not remove them. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics, pp 609\u2013614, https:\/\/doi.org\/10.18653\/v1\/n19-1061","DOI":"10.18653\/v1\/n19-1061"},{"key":"910_CR35","doi-asserted-by":"crossref","unstructured":"Hanna A, Denton E, Smart A, Smith-Loud J (2020) Towards a critical race methodology in algorithmic fairness. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 501\u2013512","DOI":"10.1145\/3351095.3372826"},{"key":"910_CR36","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Proceedings of the 30th international conference on neural information processing systems, pp 3323\u20133331"},{"key":"910_CR37","unstructured":"H\u00e9bert-Johnson U, Kim M, Reingold O, Rothblum G (2018) Multicalibration: calibration for the (computationally-identifiable) masses. In: International conference on machine learning, PMLR, pp 1939\u20131948"},{"issue":"4","key":"910_CR38","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1111\/0022-4537.00234","volume":"57","author":"ME Heilman","year":"2001","unstructured":"Heilman ME (2001) Description and prescription: how gender stereotypes prevent women\u2019s ascent up the organizational ladder. J Soc Issues 57(4):657\u2013674","journal-title":"J Soc Issues"},{"key":"910_CR39","first-page":"113","volume":"32","author":"ME Heilman","year":"2012","unstructured":"Heilman ME (2012) Gender stereotypes and workplace bias. Res Organ Behav 32:113\u2013135","journal-title":"Res Organ Behav"},{"key":"910_CR40","doi-asserted-by":"crossref","unstructured":"Hu L, Kohler-Hausmann I (2020) What\u2019s sex got to do with machine learning? In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 513","DOI":"10.1145\/3351095.3375674"},{"issue":"04","key":"910_CR41","first-page":"1","volume":"26","author":"SK Johnson","year":"2016","unstructured":"Johnson SK, Hekman DR, Chan ET (2016) If there\u2019s only one woman in your candidate pool, there\u2019s statistically no chance she\u2019ll be hired. Harv Bus Rev 26(04):1\u20137","journal-title":"Harv Bus Rev"},{"issue":"1","key":"910_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowl Inf Syst 33(1):1\u201333","journal-title":"Knowl Inf Syst"},{"key":"910_CR43","doi-asserted-by":"crossref","unstructured":"Kamiran F, Karim A, Zhang X (2012) Decision theory for discrimination-aware classification. In: 2012 IEEE 12th international conference on data mining, IEEE, pp 924\u2013929","DOI":"10.1109\/ICDM.2012.45"},{"key":"910_CR44","unstructured":"Kearns MJ, Neel S, Roth A, Wu ZS (2018) Preventing fairness gerrymandering: auditing and learning for subgroup fairness. In: Dy JG, Krause A (eds) Proceedings of the 35th international conference on machine learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10\u201315, 2018, PMLR, Proceedings of Machine Learning Research, vol 80, pp 2569\u20132577, http:\/\/proceedings.mlr.press\/v80\/kearns18a.html"},{"issue":"CSCW1","key":"910_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3449113","volume":"5","author":"O Keyes","year":"2021","unstructured":"Keyes O, May C, Carrell A (2021) You keep using that word: ways of thinking about gender in computing research. Proc ACM Human-Comput Interact 5(CSCW1):1\u201323","journal-title":"Proc ACM Human-Comput Interact"},{"key":"910_CR46","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1162\/tacl_a_00327","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar V, Bhotia TS, Kumar V, Chakraborty T (2020) Nurse is closer to woman than surgeon? mitigating gender-biased proximities in word embeddings. Trans Assoc Comput Linguist 8:486\u2013503. https:\/\/doi.org\/10.1162\/tacl_a_00327","journal-title":"Trans Assoc Comput Linguist"},{"key":"910_CR47","unstructured":"Kusner MJ, Loftus J, Russell C, Silva R (2017) Counterfactual fairness. In: Advances in neural information processing systems 30 (NIPS 2017)"},{"key":"910_CR48","doi-asserted-by":"publisher","unstructured":"Larson B (2017) Gender as a variable in natural-language processing: ethical considerations. In: Proceedings of the first ACL workshop on ethics in natural language processing, association for computational linguistics, Valencia, Spain, pp 1\u201311, https:\/\/doi.org\/10.18653\/v1\/W17-1601","DOI":"10.18653\/v1\/W17-1601"},{"issue":"3","key":"910_CR49","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1353\/tech.1999.0128","volume":"40","author":"JS Light","year":"1999","unstructured":"Light JS (1999) When computers were women. Technol Cult 40(3):455\u2013483","journal-title":"Technol Cult"},{"key":"910_CR50","unstructured":"Lipton Z, McAuley J, Chouldechova A (2018) Does mitigating ML\u2019s impact disparity require treatment disparity? In: Advances in neural information processing systems 31"},{"key":"910_CR51","doi-asserted-by":"crossref","unstructured":"Lohia PK, Ramamurthy KN, Bhide M, Saha D, Varshney KR, Puri R (2019) Bias mitigation post-processing for individual and group fairness. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 2847\u20132851","DOI":"10.1109\/ICASSP.2019.8682620"},{"issue":"6","key":"910_CR52","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1037\/a0016539","volume":"94","author":"JM Madera","year":"2009","unstructured":"Madera JM, Hebl MR, Martin RC (2009) Gender and letters of recommendation for academia: agentic and communal differences. J Appl Psychol 94(6):1591","journal-title":"J Appl Psychol"},{"key":"910_CR53","doi-asserted-by":"crossref","unstructured":"Mangheni M, Tufan H, Nkengla L, Aman B, Boonabaana B (2019) Gender norms, technology access, and women farmers\u2019 vulnerability to climate change in sub-saharan africa. In: Agriculture and ecosystem resilience in Sub Saharan Africa, Springer, pp 715\u2013728","DOI":"10.1007\/978-3-030-12974-3_32"},{"key":"910_CR54","unstructured":"Marx C, Calmon F, Ustun B (2020) Predictive multiplicity in classification. In: International conference on machine learning, PMLR, pp 6765\u20136774"},{"key":"910_CR55","unstructured":"Mikolov T, Grave \u00c9, Bojanowski P, Puhrsch C, Joulin A (2018) Advances in pre-training distributed word representations. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)"},{"key":"910_CR56","doi-asserted-by":"crossref","unstructured":"Mitchell M, Baker D, Moorosi N, Denton E, Hutchinson B, Hanna A, Gebru T, Morgenstern J (2020) Diversity and inclusion metrics in subset selection. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society, pp 117\u2013123","DOI":"10.1145\/3375627.3375832"},{"issue":"1","key":"910_CR57","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1558\/genl.v8i1.5","volume":"8","author":"R Moon","year":"2014","unstructured":"Moon R (2014) From gorgeous to grumpy: adjectives, age and gender. Gender Lang 8(1):5\u201341","journal-title":"Gender Lang"},{"key":"910_CR58","doi-asserted-by":"publisher","unstructured":"Nadeem M, Bethke A, Reddy S (2021) Stereoset: measuring stereotypical bias in pretrained language models. In: Zong C, Xia F, Li W, Navigli R (eds) Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, Association for Computational Linguistics, pp 5356\u20135371, https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.416","DOI":"10.18653\/v1\/2021.acl-long.416"},{"key":"910_CR59","doi-asserted-by":"publisher","unstructured":"Nangia N, Vania C, Bhalerao R, Bowman SR (2020) Crows-pairs: A challenge dataset for measuring social biases in masked language models. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16\u201320, 2020, Association for computational linguistics, pp 1953\u20131967, https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.154","DOI":"10.18653\/v1\/2020.emnlp-main.154"},{"key":"910_CR60","doi-asserted-by":"publisher","DOI":"10.18574\/nyu\/9781479833641.001.0001","volume-title":"Algorithms of oppression: how search engines reinforce racism","author":"SU Noble","year":"2018","unstructured":"Noble SU (2018) Algorithms of oppression: how search engines reinforce racism. NYU Press, New York"},{"key":"910_CR61","doi-asserted-by":"publisher","unstructured":"Park JH, Shin J, Fung P (2018) Reducing gender bias in abusive language detection. In: Proceedings of the 2018 conference on empirical methods in natural language processing, association for computational linguistics, Brussels, Belgium, pp 2799\u20132804, https:\/\/doi.org\/10.18653\/v1\/D18-1302","DOI":"10.18653\/v1\/D18-1302"},{"key":"910_CR62","first-page":"125","volume":"7","author":"A Peng","year":"2019","unstructured":"Peng A, Nushi B, K\u0131c\u0131man E, Inkpen K, Suri S, Kamar E (2019) What you see is what you get? the impact of representation criteria on human bias in hiring. Proc AAAI Conf Hum Comput Crowdsour 7:125\u2013134","journal-title":"Proc AAAI Conf Hum Comput Crowdsour"},{"key":"910_CR63","doi-asserted-by":"crossref","unstructured":"Peng A, Nushi B, Kiciman E, Inkpen K, Kamar E (2022) Investigations of performance and bias in human-AI teamwork in hiring. In: Proceedings of the 36th AAAI conference on artificial intelligence (AAAI 2022), AAAI","DOI":"10.1609\/aaai.v36i11.21468"},{"key":"910_CR64","unstructured":"Pleiss G, Raghavan M, Wu F, Kleinberg J, Weinberger KQ (2017) On fairness and calibration. In: Advances in neural information processing systems 30 (NIPS 2017)"},{"key":"910_CR65","doi-asserted-by":"crossref","unstructured":"Raghavan M, Barocas S, Kleinberg J, Levy K (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 469\u2013481","DOI":"10.1145\/3351095.3372828"},{"key":"910_CR66","doi-asserted-by":"publisher","unstructured":"Romanov A, De-Arteaga M, Wallach HM, Chayes JT, Borgs C, Chouldechova A, Geyik SC, Kenthapadi K, Rumshisky A, Kalai A (2019) What\u2019s in a name? reducing bias in bios without access to protected attributes. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for computational linguistics, pp 4187\u20134195, https:\/\/doi.org\/10.18653\/v1\/n19-1424","DOI":"10.18653\/v1\/n19-1424"},{"key":"910_CR67","doi-asserted-by":"publisher","unstructured":"Rudinger R, May C, Van Durme B (2017) Social bias in elicited natural language inferences. In: Proceedings of the First ACL workshop on ethics in natural language processing, association for computational linguistics, Valencia, Spain, pp 74\u201379, https:\/\/doi.org\/10.18653\/v1\/W17-1609","DOI":"10.18653\/v1\/W17-1609"},{"key":"910_CR68","doi-asserted-by":"publisher","unstructured":"Rudinger R, Naradowsky J, Leonard B, Durme BV (2018) Gender bias in coreference resolution. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1\u20136, 2018, Volume 2 (Short Papers), Association for Computational Linguistics, pp 8\u201314, https:\/\/doi.org\/10.18653\/v1\/n18-2002","DOI":"10.18653\/v1\/n18-2002"},{"key":"910_CR69","volume-title":"Perceptions of female offenders: How stereotypes and social norms affect criminal justice responses","author":"B Russell","year":"2012","unstructured":"Russell B (2012) Perceptions of female offenders: How stereotypes and social norms affect criminal justice responses. Springer Science and Business Media, Berlin"},{"key":"910_CR70","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Monedero J, Dencik L, Edwards L (2020) What does it mean to\u2019solve\u2019the problem of discrimination in hiring? social, technical and legal perspectives from the uk on automated hiring systems. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 458\u2013468","DOI":"10.1145\/3351095.3372849"},{"issue":"CSCW","key":"910_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3359246","volume":"3","author":"MK Scheuerman","year":"2019","unstructured":"Scheuerman MK, Paul JM, Brubaker JR (2019) How computers see gender. Proc ACM Human-Comput Interact 3(CSCW):1\u201333. https:\/\/doi.org\/10.1145\/3359246","journal-title":"Proc ACM Human-Comput Interact"},{"key":"910_CR72","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J (2010) Statsmodels: econometric and statistical modeling with python. In: 9th Python in science conference","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"910_CR73","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1146\/annurev-polisci-032015-010015","volume":"19","author":"M Sen","year":"2016","unstructured":"Sen M, Wasow O (2016) Race as a bundle of sticks: designs that estimate effects of seemingly immutable characteristics. Annu Rev Polit Sci 19:499\u2013522","journal-title":"Annu Rev Polit Sci"},{"issue":"5","key":"910_CR74","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s11199-008-9501-8","volume":"59","author":"SA Shields","year":"2008","unstructured":"Shields SA (2008) Gender: an intersectionality perspective. Sex Roles 59(5):301\u2013311","journal-title":"Sex Roles"},{"key":"910_CR75","unstructured":"Snyder K (2015) The resume gap: are different gender styles contributing to tech\u2019s dismal diversity. Fortune Magazine"},{"issue":"9","key":"910_CR76","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1002\/asi.24342","volume":"71","author":"L Stark","year":"2020","unstructured":"Stark L, Stanhaus A, Anthony DL (2020) i don\u2019t want someone to watch me while im working: gendered views of facial recognition technology in workplace surveillance. J Am Soc Inf Sci 71(9):1074\u20131088. https:\/\/doi.org\/10.1002\/asi.24342","journal-title":"J Am Soc Inf Sci"},{"key":"910_CR77","doi-asserted-by":"crossref","unstructured":"Swinger N, De-Arteaga M, Heffernan IV NT, Leiserson MD, Kalai AT (2019) What are the biases in my word embedding? In: Proceedings of the 2019 AAAI\/ACM conference on AI, ethics, and society, pp 305\u2013311","DOI":"10.1145\/3306618.3314270"},{"issue":"CSCW","key":"910_CR78","first-page":"1","volume":"1","author":"S Tang","year":"2017","unstructured":"Tang S, Zhang X, Cryan J, Metzger MJ, Zheng H, Zhao BY (2017) Gender bias in the job market: a longitudinal analysis. Proc ACM Human-Comput Interact 1(CSCW):1\u201319","journal-title":"Proc ACM Human-Comput Interact"},{"issue":"3","key":"910_CR79","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J et al (2020) Scipy 1.0: fundamental algorithms for scientific computing in python. Nat Methods 17(3):261\u2013272","journal-title":"Nat Methods"},{"key":"910_CR80","doi-asserted-by":"crossref","unstructured":"Wagner C, Garcia D, Jadidi M, Strohmaier M (2015) It\u2019s a man\u2019s wikipedia? assessing gender inequality in an online encyclopedia. In: Proceedings of the international AAAI conference on web and social media, vol 9","DOI":"10.1609\/icwsm.v9i1.14628"},{"key":"910_CR81","doi-asserted-by":"crossref","unstructured":"Wang T, Zhao J, Yatskar M, Chang KW, Ordonez V (2019) Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5310\u20135319","DOI":"10.1109\/ICCV.2019.00541"},{"key":"910_CR82","unstructured":"Wojcik S, Remy E (2020) The challenges of using machine learning to identify gender in images. https:\/\/www.pewresearch.org\/internet\/2019\/09\/05\/the-challenges-of-using-machine-learning-to-identify-gender-in-images\/"},{"key":"910_CR83","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M, Lhoest Q, Rush AM (2020) Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, association for computational linguistics, Online, pp 38\u201345, https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"910_CR84","doi-asserted-by":"crossref","unstructured":"Wood W, Eagly AH (2009) Gender identity. Handbook of individual differences in social behavior pp 109\u2013125","DOI":"10.1002\/9780470561119.socpsy001017"},{"key":"910_CR85","doi-asserted-by":"crossref","unstructured":"Zhang BH, Lemoine B, Mitchell M (2018) Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM conference on AI, ethics, and society, pp 335\u2013340","DOI":"10.1145\/3278721.3278779"},{"key":"910_CR86","doi-asserted-by":"publisher","unstructured":"Zhou X, Sap M, Swayamdipta S, Choi Y, Smith NA (2021) Challenges in automated debiasing for toxic language detection. In: Merlo P, Tiedemann J, Tsarfaty R (eds) Proceedings of the 16th conference of the European chapter of the association for computational linguistics: main volume, EACL 2021, Online, April 19\u201323, 2021, Association for computational linguistics, pp 3143\u20133155, https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.274","DOI":"10.18653\/v1\/2021.eacl-main.274"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00910-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00910-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00910-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T19:35:22Z","timestamp":1728761722000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00910-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"references-count":86,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["910"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00910-8","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,23]]},"assertion":[{"value":"27 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}