{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:16:09Z","timestamp":1766733369367,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441974"},{"type":"electronic","value":"9783031441981"}],"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\/978-3-031-44198-1_7","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"75-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Explainable Feature Selection Approach for\u00a0Fair Machine Learning"],"prefix":"10.1007","author":[{"given":"Zhi","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changwu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"7_CR1","unstructured":"Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias (2016). https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Dorleon, G., Megdiche, I., Bricon-Souf, N., Teste, O.: Feature selection under fairness constraints. In: Proceedings of the 37th ACM\/SIGAPP Symposium on Applied Computing, pp. 1125\u20131127 (2022)","DOI":"10.1145\/3477314.3507168"},{"key":"7_CR4","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":"7_CR5","unstructured":"Gajane, P., Pechenizkiy, M.: On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184 (2017)"},{"key":"7_CR6","unstructured":"Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., Weller, A.: The case for process fairness in learning: feature selection for fair decision making. In: NIPS Symposium on Machine Learning and the Law, Barcelona, Spain, vol. 1, p. 11 (2016)"},{"key":"7_CR7","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157\u20131182 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR8","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Huang, C., Zhang, Z., Mao, B., Yao, X.: An overview of artificial intelligence ethics. IEEE Trans. Artif. Intell. 1\u201321 (2022). https:\/\/doi.org\/10.1109\/TAI.2022.3194503","DOI":"10.1109\/TAI.2022.3194503"},{"key":"7_CR10","unstructured":"Khodadadian, S., Nafea, M., Ghassami, A., Kiyavash, N.: Information theoretic measures for fairness-aware feature selection. arXiv preprint arXiv:2106.00772 (2021)"},{"issue":"3","key":"7_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/widm.1452","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. Discov. 12(3), 1\u201359 (2022)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Lou, Y., Caruana, R., Gehrke, J., Hooker, G.: Accurate intelligible models with pairwise interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 623\u2013631. Association for Computing Machinery, New York (2013)","DOI":"10.1145\/2487575.2487579"},{"key":"7_CR13","unstructured":"Lundberg, S.M.: Explaining quantitative measures of fairness. In: Fair & Responsible AI Workshop@ CHI2020 (2020)"},{"key":"7_CR14","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"6","key":"7_CR15","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":"7_CR16","unstructured":"Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: a unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)"},{"issue":"3","key":"7_CR17","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":"7_CR18","doi-asserted-by":"crossref","unstructured":"Rehman, A.U., Nadeem, A., Malik, M.Z.: Fair feature subset selection using multiobjective genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 360\u2013363 (2022)","DOI":"10.1145\/3520304.3529061"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. Association for Computing Machinery, New York (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"7_CR20","unstructured":"Singh, M.: Fair classification under covariate shift and missing protected attribute-an investigation using related features. arXiv preprint arXiv:2204.07987 (2022)"},{"key":"7_CR21","unstructured":"Thampi, A.: Interpretable AI: Building Explainable Machine Learning Systems. Manning Publications Co. (2022)"},{"issue":"3","key":"7_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3551390","volume":"17","author":"M Wan","year":"2023","unstructured":"Wan, M., Zha, D., Liu, N., Zou, N.: In-processing modeling techniques for machine learning fairness: a survey. ACM Trans. Knowl. Discov. Data 17(3), 1\u201327 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"7_CR23","unstructured":"Woodworth, B., Gunasekar, S., Ohannessian, M.I., Srebro, N.: Learning non-discriminatory predictors. In: Conference on Learning Theory, pp. 1920\u20131953. PMLR (2017)"},{"issue":"4","key":"7_CR24","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606\u2013626 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Booth, S., Ribeiro, M.T., Shah, J.: Do feature attribution methods correctly attribute features? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 9623\u20139633 (2022)","DOI":"10.1609\/aaai.v36i9.21196"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44198-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T22:11:22Z","timestamp":1701209482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44198-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441974","9783031441981"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44198-1_7","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":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}