{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:26Z","timestamp":1729225706246,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Machine learning models have been instrumental in making decisions across domains, like mortgage lending and risk assessment in finance. However, these models have been found susceptible to biases, causing unfair decisions for a specific group of individuals. Such bias is generally based on some protected (or sensitive) attributes, such as age, sex, or race, and is still prevalent due to historical context or algorithmic bias. There have been several efforts to ensure equal opportunities for each individual\/group, based on creditworthiness, rather than any social bias. Several pre-, in- and post-processing bias mitigation techniques have been proposed. However, these techniques perform data transformation or design new constraint\/cost functions, which are task-specific, to achieve a fair prediction. Such techniques even require further access to the complete training\/testing data. This paper proposes a novel post-processing bias mitigation technique that employs a model interpretation strategy to find the responsible model weights causing the bias. Pruning only a few model weights exhibits group fairness in model predictions while maintaining competitive accuracy levels, thus aligning with the goals of fairness and efficiency in decision-making. The proposed scheme requires access to only a few data samples representing the protected attributes, without exposing the complete training data. Through extensive experiments with multiple census datasets\/methods, we demonstrate the efficacy of our approach, achieving up to a significant 50% reduction in bias while preserving the overall accuracy.<\/jats:p>","DOI":"10.3233\/faia240589","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:53:28Z","timestamp":1729169608000},"source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Bias: Model Pruning for Enhanced Model Fairness and Efficiency"],"prefix":"10.3233","author":[{"given":"Harsh","family":"Kasyap","sequence":"first","affiliation":[{"name":"The Alan Turing Institute London, UK"},{"name":"University of Warwick, Coventry, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ugur Ilker","family":"Atmaca","sequence":"additional","affiliation":[{"name":"The Alan Turing Institute London, UK"},{"name":"University of Warwick, Coventry, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michela","family":"Iezzi","sequence":"additional","affiliation":[{"name":"Bank of Italy, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toby","family":"Walsh","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carsten","family":"Maple","sequence":"additional","affiliation":[{"name":"The Alan Turing Institute London, UK"},{"name":"University of Warwick, Coventry, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:53:29Z","timestamp":1729169609000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240589","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}