{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:30:57Z","timestamp":1742913057839,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031727863"},{"type":"electronic","value":"9783031727870"}],"license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72787-0_6","type":"book-chapter","created":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:19:24Z","timestamp":1728764364000},"page":"56-66","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Positive-Sum Fairness: Leveraging Demographic Attributes to\u00a0Achieve Fair AI Outcomes Without Sacrificing Group Gains"],"prefix":"10.1007","author":[{"given":"Samia","family":"Belhadj","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0538-5522","authenticated-orcid":false,"given":"Sanguk","family":"Park","sequence":"additional","affiliation":[]},{"given":"Ambika","family":"Seth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6458-2097","authenticated-orcid":false,"given":"Hesham","family":"Dar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7701-7837","authenticated-orcid":false,"given":"Thijs","family":"Kooi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"6_CR1","unstructured":"Baumann, J., Hertweck, C., Loi, M., Heitz, C.: Distributive justice as the foundational premise of fair ML: unification, extension, and interpretation of group fairness metrics. arXiv:2206.02897 (2023)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. arXiv:1703.09207 (2017)","DOI":"10.1177\/0049124118782533"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Brown, A., Tomasev, N., Freyberg, J., Liu, Y., Karthikesalingam, A., Schrouff, J.: Detecting shortcut learning for fair medical AI using shortcut testing. arXiv:2207.10384 (2022)","DOI":"10.1038\/s41467-023-39902-7"},{"issue":"10","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.2105\/AJPH.2009.181313","volume":"100","author":"DC Burton","year":"2010","unstructured":"Burton, D.C., et al.: Socioeconomic and racial\/ethnic disparities in the incidence of bacteremic pneumonia among US adults. Am. J. Public Health 100(10), 1904\u20131911 (2010)","journal-title":"Am. J. Public Health"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Diana, E., Gill, W., Kearns, M., Kenthapadi, K., Roth, A.: Minimax group fairness: algorithms and experiments. arXiv:2011.03108 (2021)","DOI":"10.1145\/3461702.3462523"},{"issue":"6","key":"6_CR6","doi-asserted-by":"publisher","first-page":"061102","DOI":"10.1117\/1.JMI.9.6.061102","volume":"9","author":"EAM Stanley","year":"2022","unstructured":"Stanley, E.A.M., Wilms, M., Mouches, P., Forkert, N.D.: Fairness-related performance and explainability effects in deep learning models for brain image analysis. J. Med. Imaging 9(6), 061102 (2022)","journal-title":"J. Med. Imaging"},{"issue":"397","key":"6_CR7","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1080\/01621459.1987.10478410","volume":"82","author":"B Efron","year":"1987","unstructured":"Efron, B.: Better bootstrap confidence intervals. J. Am. Stat. Assoc. 82(397), 171\u2013185 (1987)","journal-title":"J. Am. Stat. Assoc."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. arXiv:1412.3756 (2015)","DOI":"10.1145\/2783258.2783311"},{"issue":"6","key":"6_CR9","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., et al.: AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4(6), e406\u2013e414 (2022)","journal-title":"Lancet Digit. Health"},{"issue":"104467","key":"6_CR10","doi-asserted-by":"publisher","first-page":"104467","DOI":"10.1016\/j.ebiom.2023.104467","volume":"89","author":"B Glocker","year":"2023","unstructured":"Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Algorithmic encoding of protected characteristics in chest X-ray disease detection models. EBioMedicine 89(104467), 104467 (2023)","journal-title":"EBioMedicine"},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Haeri, M.A., Zweig, K.A.: The crucial role of sensitive attributes in fair classification. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2993\u20133002 (2020). https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308585","DOI":"10.1109\/SSCI47803.2020.9308585"},{"key":"6_CR12","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. arXiv:1610.02413 (2016)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van\u00a0der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. arXiv:1608.06993 (2018)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR14","unstructured":"Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L.A., Mark, R.: MIMIC-IV (2023)"},{"issue":"1","key":"6_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson, A.E.W., et al.: MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10(1), 1 (2023)","journal-title":"Sci. Data"},{"key":"6_CR16","unstructured":"Johnson, A.E.W., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv:1901.07042 (2019)"},{"issue":"3","key":"6_CR17","doi-asserted-by":"publisher","first-page":"E303","DOI":"10.1148\/radiol.2020202602","volume":"297","author":"NP Joseph","year":"2020","unstructured":"Joseph, N.P., et al.: Racial and ethnic disparities in disease severity on admission chest radiographs among patients admitted with confirmed coronavirus disease 2019: a retrospective cohort study. Radiology 297(3), E303\u2013E312 (2020)","journal-title":"Radiology"},{"key":"6_CR18","unstructured":"Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. arXiv:1609.05807 (2016)"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"4581","DOI":"10.1038\/s41467-022-32186-3","volume":"13","author":"MAR Lara","year":"2022","unstructured":"Lara, M.A.R., Echeveste, R., Ferrante, E.: Addressing fairness in artificial intelligence for medical imaging. Nat. Commun. 13, 4581 (2022)","journal-title":"Nat. Commun."},{"key":"6_CR20","unstructured":"Lee, J., Brooks, C., Yu, R., Kizilcec, R.: Fairness hub technical briefs: AUC gap. arXiv:2309.12371 (2023)"},{"key":"6_CR21","unstructured":"Lee, J.K., et al.: Fair selective classification via sufficiency. In: International Conference on Machine Learning (2021). https:\/\/api.semanticscholar.org\/CorpusID:235826429"},{"issue":"2","key":"6_CR22","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10506-016-9182-5","volume":"24","author":"I \u017dliobait\u0117","year":"2016","unstructured":"\u017dliobait\u0117, I., Custers, B.: Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artif. Intell. Law 24(2), 183\u2013201 (2016). https:\/\/doi.org\/10.1007\/s10506-016-9182-5","journal-title":"Artif. Intell. Law"},{"key":"6_CR23","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv:1608.03983 (2017)"},{"key":"6_CR24","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv:1711.05101 (2019)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Mittelstadt, B., Wachter, S., Russell, C.: The unfairness of fair machine learning: levelling down and strict egalitarianism by default. arXiv:2302.02404 (2023)","DOI":"10.36645\/mtlr.30.1.unfairness"},{"key":"6_CR26","unstructured":"Mukherjee, D., Yurochkin, M., Banerjee, M., Sun, Y.: Two simple ways to learn individual fairness metrics from data. arXiv:2006.11439 (2020)"},{"key":"6_CR27","unstructured":"Petersen, E., Ferrante, E., Ganz, M., Feragen, A.: Are demographically invariant models and representations in medical imaging fair? arXiv:2305.01397 (2024)"},{"issue":"7","key":"6_CR28","doi-asserted-by":"publisher","first-page":"100790","DOI":"10.1016\/j.patter.2023.100790","volume":"4","author":"E Petersen","year":"2023","unstructured":"Petersen, E., Holm, S., Ganz, M., Feragen, A.: The path toward equal performance in medical machine learning. Patterns 4(7), 100790 (2023). https:\/\/doi.org\/10.1016\/j.patter.2023.100790","journal-title":"Patterns"},{"key":"6_CR29","unstructured":"Raff, E., Sylvester, J.: Gradient reversal against discrimination. arXiv:1807.00392 (2018)"},{"key":"6_CR30","doi-asserted-by":"publisher","unstructured":"Rajeev, C., Natarajan, K.: Data augmentation in classifying chest radiograph images (CXR) using DCGAN-CNN. In: Solanki, A., Naved, M. (eds.) GANs for Data Augmentation in Healthcare. Springer, Cham, pp. 91\u2013110 (2023). https:\/\/doi.org\/10.1007\/978-3-031-43205-7_6","DOI":"10.1007\/978-3-031-43205-7_6"},{"issue":"3\u20134","key":"6_CR31","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10689-004-9550-2","volume":"3","author":"WS Rubinstein","year":"2004","unstructured":"Rubinstein, W.S.: Hereditary breast cancer in jews. Fam. Cancer 3(3\u20134), 249\u2013257 (2004)","journal-title":"Fam. Cancer"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. arXiv:1409.0575 (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"6_CR33","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M., Chen, I., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176\u20132182 (2021). https:\/\/doi.org\/10.1038\/s41591-021-01595-0","journal-title":"Nat. Med."},{"key":"6_CR34","doi-asserted-by":"publisher","first-page":"946625","DOI":"10.3389\/fonc.2022.946625","volume":"12","author":"H Shi","year":"2022","unstructured":"Shi, H., et al.: Genomic landscape of lung adenocarcinomas in different races. Front. Oncol. 12, 946625 (2022)","journal-title":"Front. Oncol."},{"key":"6_CR35","unstructured":"Ustun, B., Liu, Y., Parkes, D.: Fairness without harm: decoupled classifiers with preference guarantees. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a097, pp. 6373\u20136382. PMLR (2019). https:\/\/proceedings.mlr.press\/v97\/ustun19a.html"},{"issue":"1","key":"6_CR36","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1159\/000509119","volume":"30","author":"B Varkey","year":"2021","unstructured":"Varkey, B.: Principles of clinical ethics and their application to practice. Med. Princ. Pract. 30(1), 17\u201328 (2021)","journal-title":"Med. Princ. Pract."},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Verma, S., Rubin, J.S.: Fairness definitions explained. In: 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare), pp.\u00a01\u20137 (2018). https:\/\/api.semanticscholar.org\/CorpusID:49561627","DOI":"10.1145\/3194770.3194776"},{"issue":"14","key":"6_CR38","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1093\/jnci\/91.14.1241","volume":"91","author":"E Warner","year":"1999","unstructured":"Warner, E., et al.: Prevalence and penetrance of BRCA1 and BRCA2 gene mutations in unselected ashkenazi jewish women with breast cancer. J. Natl. Cancer Inst. 91(14), 1241\u20131247 (1999)","journal-title":"J. Natl. Cancer Inst."},{"key":"6_CR39","doi-asserted-by":"publisher","unstructured":"Xu, Z., Li, J., Yao, Q., Li, H., Zhou, S.K.: Fairness in medical image analysis and healthcare: a literature survey. TechRxiv (2023). https:\/\/doi.org\/10.36227\/techrxiv.24324979.v1","DOI":"10.36227\/techrxiv.24324979.v1"},{"key":"6_CR40","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, H., Gichoya, J.W., Katabi, D., Ghassemi, M.: The limits of fair medical imaging AI in the wild. arXiv:2312.10083 (2023)","DOI":"10.1038\/s41591-024-03113-4"},{"key":"6_CR41","unstructured":"Zong, Y., Yang, Y., Hospedales, T.: MEDFAIR: benchmarking fairness for medical imaging. arXiv:2210.01725 (2023)"}],"container-title":["Lecture Notes in Computer Science","Ethics and Fairness in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72787-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T15:20:36Z","timestamp":1732893636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72787-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,13]]},"ISBN":["9783031727863","9783031727870"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72787-0_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,13]]},"assertion":[{"value":"13 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that there are no conflicts of interest regarding the publication of this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FAIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Fairness of AI in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2024-miccai\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}