{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:43:10Z","timestamp":1742928190398,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031401763"},{"type":"electronic","value":"9783031401770"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-40177-0_14","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"217-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Group Fairness in\u00a0Case-Based Reasoning"],"prefix":"10.1007","author":[{"given":"Shania","family":"Mitra","sequence":"first","affiliation":[]},{"given":"Ditty","family":"Mathew","sequence":"additional","affiliation":[]},{"given":"Deepak","family":"P.","sequence":"additional","affiliation":[]},{"given":"Sutanu","family":"Chakraborti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","unstructured":"An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6(6), 448\u2013452 (1976). https:\/\/doi.org\/10.1109\/TSMC.1976.4309523","DOI":"10.1109\/TSMC.1976.4309523"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s41019-021-00169-x","volume":"6","author":"SS Abraham","year":"2021","unstructured":"Abraham, S.S.: FairLOF: fairness in outlier detection. Data Sci. Eng. 6, 485\u2013499 (2021)","journal-title":"Data Sci. Eng."},{"key":"14_CR3","unstructured":"Abraham, S.S., Sundaram, S.S., et al.: Fairness in clustering with multiple sensitive attributes. EDBT (2020)"},{"key":"14_CR4","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning: Limitations and Opportunities (2019). http:\/\/www.fairmlbook.org"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 405\u2013414 (2018)","DOI":"10.1145\/3209978.3210063"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Binns, R.: On the apparent conflict between individual and group fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 514\u2013524 (2020)","DOI":"10.1145\/3351095.3372864"},{"key":"14_CR7","unstructured":"Blanzeisky, W., Cunningham, P., Kennedy, K.: Introducing a family of synthetic datasets for research on bias in machine learning (2021)"},{"key":"14_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-031-14923-8_4","volume-title":"Case-Based Reasoning Research and Development","author":"W Blanzeisky","year":"2022","unstructured":"Blanzeisky, W., Smyth, B., Cunningham, P.: Algorithmic bias and fairness in case-based reasoning. In: Keane, M.T., Wiratunga, N. (eds.) ICCBR 2022. LNAI, vol. 13405, pp. 48\u201362. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-14923-8_4"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"14_CR10","unstructured":"Chang, K.W., Prabhakaran, V., Ordonez, V.: Bias and fairness in natural language processing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts (2019)"},{"issue":"5","key":"14_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/3376898","volume":"63","author":"A Chouldechova","year":"2020","unstructured":"Chouldechova, A., Roth, A.: A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5), 82\u201389 (2020)","journal-title":"Commun. ACM"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Deepak, P., Abraham, S.S.: Fair outlier detection. In: 21th International Conference on Web Information Systems Engineering: WISE 2020, pp. 447\u2013462 (2020)","DOI":"10.1007\/978-3-030-62008-0_31"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018). https:\/\/doi.org\/10.1126\/sciadv.aao5580","DOI":"10.1126\/sciadv.aao5580"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226 (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., Burke, R., Diaz, F.: Fairness and discrimination in retrieval and recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1403\u20131404 (2019)","DOI":"10.1145\/3331184.3331380"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Hertweck, C., Heitz, C., Loi, M.: On the moral justification of statistical parity. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 747\u2013757 (2021)","DOI":"10.1145\/3442188.3445936"},{"key":"14_CR17","unstructured":"Kohavi, R.: Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: KDD (1997)"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Loop: local outlier probabilities. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1649\u20131652 (2009)","DOI":"10.1145\/1645953.1646195"},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s10791-018-9341-2","volume":"22","author":"J Kulshrestha","year":"2019","unstructured":"Kulshrestha, J., et al.: Search bias quantification: investigating political bias in social media and web search. Inf. Retrieval J. 22, 188\u2013227 (2019)","journal-title":"Inf. Retrieval J."},{"key":"14_CR20","doi-asserted-by":"publisher","unstructured":"Kunaver, M., Porl, T.: Diversity in recommender systems a survey. Know.-Based Syst. 123(C), 154\u2013162 (2017). https:\/\/doi.org\/10.1016\/j.knosys.2017.02.009","DOI":"10.1016\/j.knosys.2017.02.009"},{"issue":"3","key":"14_CR21","volume":"12","author":"T Le Quy","year":"2022","unstructured":"Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 12(3), e1452 (2022)","journal-title":"Wiley Interdisc. Rev.: Data Min. Knowl. Discovery"},{"key":"14_CR22","unstructured":"Narayanan, A.: Translation tutorial: 21 fairness definitions and their politics. In: Proceedings of the Conference on Fairness Accountability and Transparency, New York, USA, vol. 1170, p. 3 (2018)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: FairRec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of the Web Conference 2020, pp. 1194\u20131204 (2020)","DOI":"10.1145\/3366423.3380196"},{"key":"14_CR24","unstructured":"Pessach, D., Shmueli, E.: Algorithmic fairness. arXiv preprint arXiv:2001.09784 (2020)"},{"issue":"3","key":"14_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494672","volume":"55","author":"D Pessach","year":"2022","unstructured":"Pessach, D., Shmueli, E.: A review on fairness in machine learning. ACM Comput. Surv. (CSUR) 55(3), 1\u201344 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"14_CR26","doi-asserted-by":"publisher","unstructured":"Quijano-Sanchez, L., Recio-Garcia, J.A., Diaz-Agudo, B., Jimenez-Diaz, G.: Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 4(1) (2013). https:\/\/doi.org\/10.1145\/2414425.2414433","DOI":"10.1145\/2414425.2414433"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Yang, K., Gkatzelis, V., Stoyanovich, J.: Balanced ranking with diversity constraints. arXiv preprint arXiv:1906.01747 (2019)","DOI":"10.24963\/ijcai.2019\/836"},{"key":"14_CR28","doi-asserted-by":"publisher","unstructured":"Yeh, I.C., Lien, C.: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl. 36(2, Part 1), 2473\u20132480 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2007.12.020","DOI":"10.1016\/j.eswa.2007.12.020"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fa* ir: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1569\u20131578 (2017)","DOI":"10.1145\/3132847.3132938"},{"key":"14_CR30","unstructured":"Zehlike, M., Yang, K., Stoyanovich, J.: Fairness in ranking: a survey. arXiv preprint arXiv:2103.14000 (2021)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11\u201320 (2021)","DOI":"10.1145\/3404835.3462875"}],"container-title":["Lecture Notes in Computer Science","Case-Based Reasoning Research and Development"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40177-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T23:08:25Z","timestamp":1691708905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40177-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031401763","9783031401770"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40177-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"While our separate methods were designed for fairness interventions at different stages of the CBR process, it would be interesting to understand the complementarity between these methods and exploit them for application in scenarios where the user has control over all stages of the CBR process. We are considering extending this to cover multi-choice and structured decision scenarios and multi-valued sensitive attributes.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Future Work"}},{"value":"ICCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Case-Based Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberdeen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccbr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.comp.rgu.ac.uk\/ICCBR23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}