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Methodol."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems. Individual discrimination may occur when an AI model makes different decisions for two distinct individuals who are distinguishable solely according to protected attributes, such as age and race. Such instances reveal biased AI behavior, and are called Individual Discriminatory Instances (IDIs).<\/jats:p>\n                  <jats:p>\n                    In this article, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing. Previous studies mainly used random initial seeds to this end. However, this phase is crucial, as these seeds are the basis of the follow-up IDI generation. We dubbed our proposed seed selection approach\n                    <jats:italic toggle=\"yes\">I&amp;D<\/jats:italic>\n                    . It generates a large number of initial IDIs exhibiting a great diversity, aiming at improving the overall performance of fairness testing.\n                  <\/jats:p>\n                  <jats:p>\n                    Our empirical study reveals that\n                    <jats:italic toggle=\"yes\">I&amp;D<\/jats:italic>\n                    is able to produce a larger number of IDIs with respect to four state-of-the-art IDI generation approaches, generating 1.86X more IDIs on average. When using the IDIs generated with\n                    <jats:italic toggle=\"yes\">I&amp;D<\/jats:italic>\n                    for retraining a machine learning model, the percentage of IDIs in the input space\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{I}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    is decreased by 24.9% on average, implying that\n                    <jats:italic toggle=\"yes\">I&amp;D<\/jats:italic>\n                    is effective for improving the model\u2019s fairness.\n                  <\/jats:p>","DOI":"10.1145\/3737697","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T12:58:03Z","timestamp":1749041883000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9316-7250","authenticated-orcid":false,"given":"Zhao","family":"Tian","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6303-1731","authenticated-orcid":false,"given":"Minghua","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-5909","authenticated-orcid":false,"given":"Max","family":"Hort","sequence":"additional","affiliation":[{"name":"Simula Research Laboratory, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-442X","authenticated-orcid":false,"given":"Federica","family":"Sarro","sequence":"additional","affiliation":[{"name":"University College London, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China and The University of Newcastle, Callaghan, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3056-9962","authenticated-orcid":false,"given":"Junjie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. 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