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<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="robots" content="max-image-preview:large"> <meta name="access" content="Yes"> <meta name="360-site-verification" content="1268d79b5e96aecf3ff2a7dac04ad990" /> <title>Three naive Bayes approaches for discrimination-free classification | Data Mining and Knowledge Discovery </title> <meta name="twitter:site" content="@SpringerLink"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image:alt" content="Content cover image"/> <meta name="twitter:title" content="Three naive Bayes approaches for discrimination-free classification"/> <meta name="twitter:description" content="Data Mining and Knowledge Discovery - In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to..."/> <meta name="twitter:image" content="https://media.springernature.com/full/springer-static/cover-hires/journal/10618"/> <meta name="journal_id" content="10618"/> <meta name="dc.title" content="Three naive Bayes approaches for discrimination-free classification"/> <meta name="dc.source" content="Data Mining and Knowledge Discovery 2010 21:2"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2010-07-27"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2010 The Author(s)"/> <meta name="dc.rights" content="2010 The Author(s)"/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data."/> <meta name="prism.issn" content="1573-756X"/> <meta name="prism.publicationName" content="Data Mining and Knowledge Discovery"/> <meta name="prism.publicationDate" content="2010-07-27"/> <meta name="prism.volume" content="21"/> <meta name="prism.number" content="2"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="277"/> <meta name="prism.endingPage" content="292"/> <meta name="prism.copyright" content="2010 The Author(s)"/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s10618-010-0190-x"/> <meta name="prism.doi" content="doi:10.1007/s10618-010-0190-x"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s10618-010-0190-x.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s10618-010-0190-x"/> <meta name="citation_journal_title" content="Data Mining and Knowledge Discovery"/> <meta name="citation_journal_abbrev" content="Data Min Knowl Disc"/> <meta name="citation_publisher" content="Springer US"/> <meta name="citation_issn" content="1573-756X"/> <meta name="citation_title" content="Three naive Bayes approaches for discrimination-free classification"/> <meta name="citation_volume" content="21"/> <meta name="citation_issue" content="2"/> <meta name="citation_publication_date" content="2010/09"/> <meta name="citation_online_date" content="2010/07/27"/> <meta name="citation_firstpage" content="277"/> <meta name="citation_lastpage" content="292"/> <meta name="citation_article_type" content="Article"/> <meta name="citation_fulltext_world_readable" content=""/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s10618-010-0190-x"/> <meta name="DOI" content="10.1007/s10618-010-0190-x"/> <meta name="size" content="60060"/> <meta name="citation_doi" content="10.1007/s10618-010-0190-x"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s10618-010-0190-x&api_key="/> <meta name="description" content="In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect"/> <meta name="dc.creator" content="Calders, Toon"/> <meta name="dc.creator" content="Verwer, Sicco"/> <meta name="dc.subject" content="Data Mining and Knowledge Discovery"/> <meta name="dc.subject" content="Artificial Intelligence"/> <meta name="dc.subject" content="Information Storage and Retrieval"/> <meta name="dc.subject" content="Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences"/> <meta name="citation_reference" content="Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. 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In: Proceedings of ACM SIGKDD"/> <meta name="citation_reference" content="Pedreschi D, Ruggieri S, Turini F (2009) Measuring discrimination in socially-sensitive decision records. In: Proceedings of SIAM DM"/> <meta name="citation_author" content="Calders, Toon"/> <meta name="citation_author_institution" content="Eindhoven University of Technology, Eindhoven, Netherlands"/> <meta name="citation_author" content="Verwer, Sicco"/> <meta name="citation_author_email" content="s.verwer@tue.nl"/> <meta name="citation_author_institution" content="Eindhoven University of Technology, Eindhoven, Netherlands"/> <meta name="format-detection" content="telephone=no"/> <meta name="citation_cover_date" content="2010/09/01"/> <meta property="og:url" content="https://link.springer.com/article/10.1007/s10618-010-0190-x"/> <meta property="og:type" content="article"/> <meta property="og:site_name" content="SpringerLink"/> <meta property="og:title" content="Three naive Bayes approaches for discrimination-free classification - Data Mining and Knowledge Discovery"/> <meta property="og:description" content="In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data."/> <meta property="og:image" content="https://media.springernature.com/full/springer-static/cover-hires/journal/10618"/> <meta name="format-detection" content="telephone=no"> <link rel="apple-touch-icon" sizes="180x180" href=/oscar-static/img/favicons/darwin/apple-touch-icon-92e819bf8a.png> <link rel="icon" type="image/png" sizes="192x192" href=/oscar-static/img/favicons/darwin/android-chrome-192x192-6f081ca7e5.png> <link rel="icon" type="image/png" sizes="32x32" href=/oscar-static/img/favicons/darwin/favicon-32x32-1435da3e82.png> <link rel="icon" type="image/png" sizes="16x16" href=/oscar-static/img/favicons/darwin/favicon-16x16-ed57f42bd2.png> <link rel="shortcut icon" data-test="shortcut-icon" href=/oscar-static/img/favicons/darwin/favicon-c6d59aafac.ico> <meta name="theme-color" content="#e6e6e6">   <link rel="stylesheet" media="print" href=/oscar-static/app-springerlink/css/print-b8af42253b.css> 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Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. 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class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.</p></div></div></section> <div data-test="cobranding-download"> <div class="note test-pdf-link" id="cobranding-and-download-availability-text"> <section aria-labelledby="preview" class="app-pdf-preview"> <div class="c-article-section" id="preview-section"> <h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="preview">Article PDF</h2> <div class="c-article-section__content" id="preview-content"> <iframe src="/content/pdf/10.1007/s10618-010-0190-x.pdf" class="app-pdf-preview__frame" title="Article PDF"></iframe> </div> </div> </section> <div class="c-article-access-provider" aria-hidden="true" data-component="provided-by-box"> <p class="c-article-access-provider__text"> <a href="/content/pdf/10.1007/s10618-010-0190-x.pdf" class="c-pdf-download__link" style="display: inline; padding:0px!important;" target="_blank" rel="noopener" data-track="click" data-track-action="download pdf" data-track-label="inline link" download>Download</a> to read the full article text </p> </div> </div> </div> <section aria-labelledby="inline-recommendations" data-title="Inline Recommendations" class="c-article-recommendations" data-track-component="inline-recommendations"> <h3 class="c-article-recommendations-title" id="inline-recommendations">Similar content being viewed by others</h3> <div class="c-article-recommendations-list"> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-3-642-30487-3?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-3-642-30487-3_12?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 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In: Proceedings of SIAM DM</p></li></ul><p class="c-article-references__download u-hide-print"><a data-track="click" data-track-action="download citation references" data-track-label="link" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s10618-010-0190-x?format=refman&flavour=references">Download references<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p></div></div></div></section></div><section data-title="Acknowledgements"><div class="c-article-section" id="Ack1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Ack1">Acknowledgements</h2><div class="c-article-section__content" id="Ack1-content"><p>This research was partially funded by the Netherlands Organisation for Scientific Research (NWO) through the <i>Responsible Innovation project grant Data Mining Without Discrimination</i> (KMVI-08-29).</p> <h3 class="c-article__sub-heading">Open Access</h3> <p>This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.</p> </div></div></section><section aria-labelledby="author-information" data-title="Author information"><div class="c-article-section" id="author-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="author-information">Author information</h2><div class="c-article-section__content" id="author-information-content"><h3 class="c-article__sub-heading" id="affiliations">Authors and Affiliations</h3><ol class="c-article-author-affiliation__list"><li id="Aff1"><p class="c-article-author-affiliation__address">Eindhoven University of Technology, Eindhoven, Netherlands</p><p class="c-article-author-affiliation__authors-list">Toon Calders & Sicco Verwer</p></li></ol><div class="u-js-hide u-hide-print" data-test="author-info"><span class="c-article__sub-heading">Authors</span><ol class="c-article-authors-search u-list-reset"><li id="auth-Toon-Calders-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Toon Calders</span><div class="c-article-authors-search__list"><div class="c-article-authors-search__item c-article-authors-search__list-item--left"><a href="/search?dc.creator=Toon%20Calders" class="c-article-button" data-track="click" data-track-action="author link - 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