{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T05:05:02Z","timestamp":1764133502609,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61862060","61462079","61562086"],"award-info":[{"award-number":["61862060","61462079","61562086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group for outlier detection, outlier detection methods are likely to propagate statistical biases in the data and generate unfair results. Our work focuses on studying the fairness of outlier detection. We characterize the properties of fair outlier detection and propose an appropriate outlier detection method that combines adversarial representation learning and the LOF algorithm (AFLOF). Unlike the FairLOF method that adds fairness constraints to the LOF algorithm, AFLOF uses adversarial networks to learn the optimal representation of the original data while hiding the sensitive attribute in the data. We introduce a dynamic weighting module that assigns lower weight values to data objects with higher local outlier factors to eliminate the influence of outliers on representation learning. Lastly, we conduct comparative experiments on six publicly available datasets. The results demonstrate that compared to the density-based LOF method and the recently proposed FairLOF method, our proposed AFLOF method has a significant advantage in both the outlier detection performance and fairness.<\/jats:p>","DOI":"10.3390\/sym14020347","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:26:48Z","timestamp":1644442008000},"page":"347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fair Outlier Detection Based on Adversarial Representation Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-9485","authenticated-orcid":false,"given":"Shu","family":"Li","sequence":"first","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830046, China"}]},{"given":"Jiong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Xusheng","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Yi","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830046, China"}]},{"given":"Rui","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","first-page":"671","article-title":"Big data\u2019s disparate impact","volume":"104","author":"Barocas","year":"2016","journal-title":"Calif. Law Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1108\/JICES-05-2018-0050","article-title":"Race, again: How face recognition technology reinforces racial discrimination","volume":"17","author":"Bacchini","year":"2019","journal-title":"J. Inf. Commun. Ethics Soc."},{"key":"ref_3","unstructured":"Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., and Kalai, A.T. (2016, January 5\u201310). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Proceedings of the 30th Annual Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_4","unstructured":"Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., and Joachims, T. (2016, January 19\u201324). Recommendations as treatments: Debiasing learning and evaluation. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_5","unstructured":"Huang, L., and Vishnoi, N. (2019, January 9\u201315). Stable and fair classification. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_6","unstructured":"Zafar, M.B., Valera, I., Rogriguez, M.G., and Gummadi, K.P. (2017, January 20\u201322). Fairness constraints: Mechanisms for fair classification. Proceedings of the 20th International Conference on Artificial Intelligence and Statistic, Fort Lauderdale, FL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, P., Zhao, H., and Liu, H. (2020, January 14\u201319). Deep fair clustering for visual learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00909"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1287\/mnsc.2018.3093","article-title":"Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads","volume":"65","author":"Lambrecht","year":"2019","journal-title":"Manag. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kang, J., He, J., Maciejewski, R., and Tong, H. (2020, January 6\u201310). InFoRM: Individual fairness on graph mining. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA.","DOI":"10.1145\/3394486.3403080"},{"key":"ref_10","unstructured":"Li, B., Li, L., Sun, A., Wang, C., and Wang, Y. (2021, January 18\u201324). Approximate group fairness for clustering. Proceedings of the 38th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kearns, M., Neel, S., Roth, A., and Wu, Z.S. (2019, January 29\u201331). An empirical study of rich subgroup fairness for machine learning. Proceedings of the Conference on Fairness, Accountability, and Transparency, Atlanta, GA, USA.","DOI":"10.1145\/3287560.3287592"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chiappa, S. (February, January 27). Path-specific counterfactual fairness. Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.33017801"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., and Chi, E.H. (2019, January 4\u20138). Fairness in recommendation ranking through pairwise comparisons. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330745"},{"key":"ref_14","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., and Mitchell, M. (2018, January 2\u20133). Mitigating unwanted biases with adversarial learning. Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, New Orleans, LA, USA.","DOI":"10.1145\/3278721.3278779"},{"key":"ref_16","unstructured":"Madras, D., Creager, E., Pitassi, T., and Zemel, R. (2018, January 10\u201315). Learning adversarially fair and transferable representations. Proceedings of the 35th International Conference on Machine Learning, Stockholmsm\u00e4ssan, Stockholm, Sweden."},{"key":"ref_17","unstructured":"Davidson, I., and Ravi, S.S. (September, January 29). A framework for determining the fairness of outlier detection. Proceedings of the 24th European Conference on Artificial Intelligence, Online and Santiago de Compostela, Spain."},{"key":"ref_18","unstructured":"Deepak, P., and Abraham, S.S. (2020, January 20\u201324). Fair outlier detection. Proceedings of the 21st International Conference on Web Information Systems Engineering, Leiden, South Holland, Nederland."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Garg, P., Villasenor, J., and Foggo, V. (2020, January 10\u201313). Fairness metrics: A comparative analysis. Proceedings of the 2020 IEEE International Conference on Big Data, Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378025"},{"key":"ref_20","unstructured":"Hardt, M., Price, E., and Srebro, N. (2016, January 5\u201310). Equality of opportunity in supervised learning. Proceedings of the 30th Annual Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104344","DOI":"10.1016\/j.dib.2019.104344","article-title":"Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico","volume":"25","author":"Palechor","year":"2019","journal-title":"Data Brief"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fehrman, E., Muhammad, A.K., Mirkes, E.M., Egan, V., and Gorban, A.N. (2017). The five factor model of personality and evaluation of drug consumption risk. Data Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-55723-6_18"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2021, September 10). Machine Bias. Risk Assessments in Criminal Sentencing. Available online: https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing.","DOI":"10.1201\/9781003278290-37"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","article-title":"The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients","volume":"36","author":"Yeh","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_25","unstructured":"Cortez, P., and Silva, A. (2008, January 9\u201313). Using data mining to predict secondary school student performance. Proceedings of the 5th Future Business Technology Conference, Porto, Portugal."},{"key":"ref_26","unstructured":"Dua, D., and Graff, C. (2021, September 10). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0049124118782533","article-title":"Fairness in criminal justice risk assessments: The state of the art","volume":"50","author":"Berk","year":"2021","journal-title":"Sociol. Methods Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Jarray, F., and El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177678"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.ins.2019.06.064","article-title":"Three-way confusion matrix for classification: A measure driven view","volume":"507","author":"Xu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_31","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 7\u20139). Unsupervised domain adaptation by backpropagation. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_32","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the 29th Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., and Sander, J. (2000, January 16\u201318). LOF: Identifying Density-Based Local Outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335388"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:04Z","timestamp":1760134624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["sym14020347"],"URL":"https:\/\/doi.org\/10.3390\/sym14020347","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,2,9]]}}}