{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:26:26Z","timestamp":1743092786205,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031768262"},{"type":"electronic","value":"9783031768279"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-76827-9_3","type":"book-chapter","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T20:14:40Z","timestamp":1735589680000},"page":"38-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Are Fair Machine Learning Models More Useful?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5304-2576","authenticated-orcid":false,"given":"Anurata Prabha","family":"Hridi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3758-7357","authenticated-orcid":false,"given":"Benjamin","family":"Watson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Awasthi, P., Beutel, A., Kleindessner, M., Morgenstern, J., Wang, X.: Evaluating fairness of machine learning models under uncertain and incomplete information. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 206\u2013214 (2021)","DOI":"10.1145\/3442188.3445884"},{"issue":"2","key":"3_CR2","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1080\/00031305.2021.1952897","volume":"76","author":"P Besse","year":"2022","unstructured":"Besse, P., del Barrio, E., Gordaliza, P., Loubes, J.M., Risser, L.: A survey of bias in machine learning through the prism of statistical parity. Am. Stat. 76(2), 188\u2013198 (2022)","journal-title":"Am. Stat."},{"key":"3_CR3","unstructured":"Bird, S., et al.: Fairlearn: a toolkit for assessing and improving fairness in AI. Microsoft, Technical report MSR-TR-2020-32 (2020)"},{"issue":"10","key":"3_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0239947","volume":"15","author":"C Blease","year":"2020","unstructured":"Blease, C., Kharko, A., Locher, C., DesRoches, C.M., Mandl, K.D.: US primary care in 2029: a Delphi survey on the impact of machine learning. PLoS ONE 15(10), e0239947 (2020)","journal-title":"PLoS ONE"},{"key":"3_CR5","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)"},{"key":"3_CR6","first-page":"71","volume":"31","author":"V Bonnardel","year":"2002","unstructured":"Bonnardel, V., Miller, S., Wardle, L., Drews, E.: Gender differences in colour-naming task. Perception 31, 71 (2002)","journal-title":"Perception"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/0093-934X(85)90029-X","volume":"26","author":"MH Bornstein","year":"1985","unstructured":"Bornstein, M.H.: On the development of color naming in young children: data and theory. Brain Lang. 26(1), 72\u201393 (1985)","journal-title":"Brain Lang."},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Brewer, R.N., Harrington, C., Heldreth, C.: Envisioning equitable speech technologies for black older adults. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 379\u2013388 (2023)","DOI":"10.1145\/3593013.3594005"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Cai, W., et al.: Adaptive sampling strategies to construct equitable training datasets. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1467\u20131478 (2022)","DOI":"10.1145\/3531146.3533203"},{"key":"3_CR10","unstructured":"Caton, S., Haas, C.: Fairness in machine learning: a survey. arXiv preprint arXiv:2010.04053 (2020)"},{"key":"3_CR11","unstructured":"Celis, L.E., Deshpande, A., Kathuria, T., Vishnoi, N.K.: How to be fair and diverse? arXiv preprint arXiv:1610.07183 (2016)"},{"issue":"3","key":"3_CR12","first-page":"327","volume":"53","author":"A Chapanis","year":"1965","unstructured":"Chapanis, A.: Color names for color space. Am. Sci. 53(3), 327\u2013346 (1965)","journal-title":"Am. Sci."},{"key":"3_CR13","unstructured":"Cook, A.M., Polgar, J.M.: Assistive Technologies-e-Book: Principles and Practice. Elsevier Health Sciences (2014)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 797\u2013806 (2017)","DOI":"10.1145\/3097983.3098095"},{"issue":"2","key":"3_CR15","first-page":"1","volume":"60","author":"PR Daugherty","year":"2019","unstructured":"Daugherty, P.R., Wilson, H.J., Chowdhury, R.: Using artificial intelligence to promote diversity. MIT Sloan Manag. Rev. 60(2), 1 (2019)","journal-title":"MIT Sloan Manag. Rev."},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"DiCiccio, C., Hsu, B., Yu, Y., Nandy, P., Basu, K.: Detection and mitigation of algorithmic bias via predictive parity. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1801\u20131816 (2023)","DOI":"10.1145\/3593013.3594117"},{"key":"3_CR17","unstructured":"Digital, M., the McKinsey Institute\u00a0for Black Economic\u00a0Mobility: the impact of generative AI on black communities. https:\/\/www.mckinsey.com\/bem\/our-insights\/the-impact-of-generative-ai-on-black-communities"},{"key":"3_CR18","unstructured":"Dutta, S., Wei, D., Yueksel, H., Chen, P.Y., Liu, S., Varshney, K.: Is there a trade-off between fairness and accuracy? A perspective using mismatched hypothesis testing. In: International Conference on Machine Learning, pp. 2803\u20132813. PMLR (2020)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Fider, N.A., Komarova, N.L.: Differences in color categorization manifested by males and females: a quantitative world color survey study. Palgrave Commun. 5(1) (2019)","DOI":"10.1057\/s41599-019-0341-7"},{"issue":"2","key":"3_CR20","doi-asserted-by":"publisher","first-page":"114","DOI":"10.2307\/455532","volume":"65","author":"J Frank","year":"1990","unstructured":"Frank, J.: Gender differences in color naming: direct mail order advertisements. Am. Speech 65(2), 114\u2013126 (1990)","journal-title":"Am. Speech"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Goel, N., Faltings, B.: Crowdsourcing with fairness, diversity and budget constraints. In: Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 297\u2013304 (2019)","DOI":"10.1145\/3306618.3314282"},{"issue":"1","key":"3_CR22","doi-asserted-by":"publisher","first-page":"27","DOI":"10.2466\/pms.1995.80.1.27","volume":"80","author":"KS Greene","year":"1995","unstructured":"Greene, K.S., Gynther, M.D.: Blue versus periwinkle: color identification and gender. Percept. Mot. Skills 80(1), 27\u201332 (1995)","journal-title":"Percept. Mot. Skills"},{"key":"3_CR23","unstructured":"Gu, J., Oelke, D.: Understanding bias in machine learning. arXiv preprint arXiv:1909.01866 (2019)"},{"key":"3_CR24","unstructured":"Havasi, C., Speer, R., Holmgren, J.: Automated color selection using semantic knowledge. In: 2010 AAAI Fall Symposium Series (2010)"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Heer, J., Stone, M.: Color naming models for color selection, image editing and palette design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1007\u20131016 (2012)","DOI":"10.1145\/2207676.2208547"},{"key":"3_CR26","unstructured":"Hort, M.: Investigating trade-offs for fair machine learning systems. Ph.D. thesis, UCL (University College London) (2023)"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Hube, C., Fetahu, B., Gadiraju, U.: Understanding and mitigating worker biases in the crowdsourced collection of subjective judgments. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1\u201312 (2019)","DOI":"10.1145\/3290605.3300637"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Iosifidis, V., Fetahu, B., Ntoutsi, E.: Fae: a fairness-aware ensemble framework. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1375\u20131380. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9006487"},{"issue":"2","key":"3_CR29","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.tics.2005.12.007","volume":"10","author":"P Kay","year":"2006","unstructured":"Kay, P., Regier, T.: Language, thought and color: recent developments. Trends Cogn. Sci. 10(2), 51\u201354 (2006)","journal-title":"Trends Cogn. Sci."},{"key":"3_CR30","unstructured":"Li, P.: Harmonizing Fairness with Utility in Data and Learning. Ph.D. thesis, Brandeis University, Graduate School of Arts & Sciences (2024)"},{"key":"3_CR31","unstructured":"Li, P., Liu, H.: Achieving fairness at no utility cost via data reweighing with influence. In: International Conference on Machine Learning, pp. 12917\u201312930. PMLR (2022)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Lin, H., Luo, M.R., MacDonald, L.W., Tarrant, A.W.: A cross-cultural colour-naming study. Part I: using an unconstrained method. Color Research & Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Fran\u00e7ais de la Couleur 26(1), 40\u201360 (2001)","DOI":"10.1002\/1520-6378(200102)26:1<40::AID-COL5>3.0.CO;2-X"},{"issue":"1","key":"3_CR33","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/TMM.2013.2285526","volume":"16","author":"S Liu","year":"2013","unstructured":"Liu, S., et al.: Fashion parsing with weak color-category labels. IEEE Trans. Multimedia 16(1), 253\u2013265 (2013)","journal-title":"IEEE Trans. Multimedia"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"MacLaury, R., Paramei, G., Dedrick, D.: Anthropology of Color: Interdisciplinary Multilevel Modeling. John Benjamins Publishing Company (2007)","DOI":"10.1075\/z.137"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Maheshwari, P., Ghuhan, M., Vinay, V.: Learning colour representations of search queries. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1389\u20131398 (2020)","DOI":"10.1145\/3397271.3401095"},{"key":"3_CR36","unstructured":"Mayor, T.: 3 challenges for chief data officers in finance. https:\/\/mitsloan.mit.edu\/ideas-made-to-matter\/3-challenges-chief-data-officers-finance"},{"issue":"6","key":"3_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3_CR38","unstructured":"Menon, A.K., Williamson, R.C.: The cost of fairness in binary classification. In: Conference on Fairness, Accountability and Transparency, pp. 107\u2013118. PMLR (2018)"},{"key":"3_CR39","unstructured":"Munroe, R.: Color survey results. https:\/\/blog.xkcd.com\/2010\/05\/03\/color-survey-results\/"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Mylonas, D., Paramei, G.V., MacDonald, L.: Gender differences in colour naming. Colour studies: a broad spectrum, pp. 225\u2013239 (2014)","DOI":"10.1075\/z.191.15myl"},{"issue":"3","key":"3_CR41","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1177\/002383098202500304","volume":"25","author":"RH Nowaczyk","year":"1982","unstructured":"Nowaczyk, R.H.: Sex-related differences in the color lexicon. Lang. Speech 25(3), 257\u2013265 (1982)","journal-title":"Lang. Speech"},{"issue":"1","key":"3_CR42","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/TVCG.2015.2467471","volume":"22","author":"V Setlur","year":"2015","unstructured":"Setlur, V., Stone, M.C.: A linguistic approach to categorical color assignment for data visualization. IEEE Trans. Visual Comput. Graph. 22(1), 698\u2013707 (2015)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"3_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-50347-5_13","volume-title":"Image Analysis and Recognition","author":"J Simon","year":"2020","unstructured":"Simon, J., Bilodeau, G.-A., Steele, D., Mahadik, H.: Color inference from semantic labeling for person search in videos. In: Campilho, A., Karray, F., Wang, Z. (eds.) ICIAR 2020. LNCS, vol. 12131, pp. 139\u2013151. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50347-5_13"},{"issue":"4","key":"3_CR44","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1145\/3338283","volume":"26","author":"K Spiel","year":"2019","unstructured":"Spiel, K., Haimson, O.L., Lottridge, D.: How to do better with gender on surveys: a guide for HCI researchers. Interactions 26(4), 62\u201365 (2019)","journal-title":"Interactions"},{"issue":"9","key":"3_CR45","first-page":"373","volume":"22","author":"NA Steckler","year":"1980","unstructured":"Steckler, N.A., Cooper, W.E.: Sex differences in color naming of unisex apparel. Anthropol. Linguist. 22(9), 373\u2013381 (1980)","journal-title":"Anthropol. Linguist."},{"key":"3_CR46","unstructured":"Tang, H., Cheng, L., Liu, N., Du, M.: A theoretical approach to characterize the accuracy-fairness trade-off pareto frontier. arXiv preprint arXiv:2310.12785 (2023)"},{"key":"3_CR47","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, Z., He, J.: Fairif: boosting fairness in deep learning via influence functions with validation set sensitive attributes. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 721\u2013730 (2024)","DOI":"10.1145\/3616855.3635844"},{"key":"3_CR48","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/BF03399660","volume":"11","author":"H Wijk","year":"1999","unstructured":"Wijk, H., Berg, S., Sivik, L., Steen, B.: Color discrimination, color naming and color preferences in 80-year olds. Aging Clin. Exp. Res. 11, 176\u2013185 (1999)","journal-title":"Aging Clin. Exp. Res."},{"key":"3_CR49","doi-asserted-by":"crossref","unstructured":"Zafar, M.B., Valera, I., Gomez\u00a0Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1171\u20131180 (2017)","DOI":"10.1145\/3038912.3052660"},{"key":"3_CR50","doi-asserted-by":"crossref","unstructured":"Zaragoza, I.E.: Colour and gender: language nuances. Feminismo-s (38), 115 (2021)","DOI":"10.14198\/fem.2021.38.05"},{"key":"3_CR51","unstructured":"Zhang, S., et al.: Towards better fairness-utility trade-off: a comprehensive measurement-based reinforcement learning framework. arXiv preprint arXiv:2307.11379 (2023)"},{"key":"3_CR52","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116\u20131124 (2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"3_CR53","doi-asserted-by":"crossref","unstructured":"Zou, J., Schiebinger, L.: AI can be sexist and racist-it\u2019s time to make it fair (2018)","DOI":"10.1038\/d41586-018-05707-8"}],"container-title":["Lecture Notes in Computer Science","HCI International 2024 \u2013 Late Breaking Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76827-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T21:07:25Z","timestamp":1735592845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76827-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031768262","9783031768279"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76827-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}