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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>Data preprocessing techniques for classification without discrimination | Knowledge and Information Systems</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="Data preprocessing techniques for classification without discrimination"/> <meta name="twitter:description" content="Knowledge and Information Systems - Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination;..."/> <meta name="twitter:image" content="https://media.springernature.com/full/springer-static/cover-hires/journal/10115"/> <meta name="journal_id" content="10115"/> <meta name="dc.title" content="Data preprocessing techniques for classification without discrimination"/> <meta name="dc.source" content="Knowledge and Information Systems 2011 33:1"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2011-12-03"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2011 The Author(s)"/> <meta name="dc.rights" content="2011 The Author(s)"/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data."/> <meta name="prism.issn" content="0219-3116"/> <meta name="prism.publicationName" content="Knowledge and Information Systems"/> <meta name="prism.publicationDate" content="2011-12-03"/> <meta name="prism.volume" content="33"/> <meta name="prism.number" content="1"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="1"/> <meta name="prism.endingPage" content="33"/> <meta name="prism.copyright" content="2011 The Author(s)"/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s10115-011-0463-8"/> <meta name="prism.doi" content="doi:10.1007/s10115-011-0463-8"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s10115-011-0463-8.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s10115-011-0463-8"/> <meta name="citation_journal_title" content="Knowledge and Information Systems"/> <meta name="citation_journal_abbrev" content="Knowl Inf Syst"/> <meta name="citation_publisher" content="Springer-Verlag"/> <meta name="citation_issn" content="0219-3116"/> <meta name="citation_title" content="Data preprocessing techniques for classification without discrimination"/> <meta name="citation_volume" content="33"/> <meta name="citation_issue" content="1"/> <meta name="citation_publication_date" content="2012/10"/> <meta name="citation_online_date" content="2011/12/03"/> <meta name="citation_firstpage" content="1"/> <meta name="citation_lastpage" content="33"/> <meta name="citation_article_type" content="Regular Paper"/> <meta name="citation_fulltext_world_readable" content=""/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s10115-011-0463-8"/> <meta name="DOI" content="10.1007/s10115-011-0463-8"/> <meta name="size" content="130054"/> <meta name="citation_doi" content="10.1007/s10115-011-0463-8"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s10115-011-0463-8&api_key="/> <meta name="description" content="Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination"/> <meta name="dc.creator" content="Kamiran, Faisal"/> <meta name="dc.creator" content="Calders, Toon"/> <meta name="dc.subject" content="Information Systems and Communication Service"/> <meta name="dc.subject" content="Database Management"/> <meta name="dc.subject" content="Data Mining and Knowledge Discovery"/> <meta name="dc.subject" content="Information Storage and Retrieval"/> <meta name="dc.subject" content="Information Systems Applications (incl. 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Knowl Inf Syst, 1–13"/> <meta name="citation_author" content="Kamiran, Faisal"/> <meta name="citation_author_email" content="faisal.kamiran@gmail.com"/> <meta name="citation_author_institution" content="Eindhoven, The Netherlands"/> <meta name="citation_author" content="Calders, Toon"/> <meta name="citation_author_email" content="t.calders@tue.nl"/> <meta name="citation_author_institution" content="Eindhoven, The Netherlands"/> <meta name="format-detection" content="telephone=no"/> <meta name="citation_cover_date" content="2012/10/01"/> <meta property="og:url" content="https://link.springer.com/article/10.1007/s10115-011-0463-8"/> <meta property="og:type" content="article"/> <meta property="og:site_name" content="SpringerLink"/> <meta property="og:title" content="Data preprocessing techniques for classification without discrimination - Knowledge and Information Systems"/> <meta property="og:description" content="Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data."/> <meta property="og:image" content="https://media.springernature.com/full/springer-static/cover-hires/journal/10115"/> <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" 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The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data.","datePublished":"2011-12-03T00:00:00Z","dateModified":"2011-12-03T00:00:00Z","pageStart":"1","pageEnd":"33","license":"https://creativecommons.org/licenses/by-nc/2.0","sameAs":"https://doi.org/10.1007/s10115-011-0463-8","keywords":["Classification","Preprocessing","Discrimination-aware data mining","Information Systems and Communication Service","Database Management","Data Mining and Knowledge Discovery","Information Storage and Retrieval","Information Systems Applications (incl. 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for classification without discrimination </div> <div data-test="inCoD" data-track-context="sticky banner"> <div class="c-pdf-container"> <div class="c-pdf-download u-clear-both u-mb-16"> <a href="/content/pdf/10.1007/s10115-011-0463-8.pdf" class="u-button u-button--full-width u-button--primary u-justify-content-space-between c-pdf-download__link" data-article-pdf="true" data-readcube-pdf-url="true" data-test="pdf-link" data-draft-ignore="true" data-track="content_download" data-track-type="article pdf download" data-track-action="download pdf" data-track-label="button" data-track-external download> <span class="c-pdf-download__text">Download PDF</span> <svg aria-hidden="true" focusable="false" width="16" height="16" class="u-icon"><use xlink:href="#icon-eds-i-download-medium"/></svg> </a> </div> </div> </div> </div> </div> <div class="c-article-header"> <header> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" 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data-article-body="true" data-track-component="article body" class="c-article-body"> <section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div 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>Recently, the following <i>Discrimination-Aware Classification Problem</i> was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward <i>sensitive attributes</i> such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being <i>suppression</i> of the sensitive attribute, <i>massaging</i> the dataset by changing class labels, and <i>reweighing</i> or <i>resampling</i> the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on 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/s10115-011-0463-8.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/s10115-011-0463-8.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/w215h120/springer-static/image/art%3A10.1007%2Fs00500-024-09847-0/MediaObjects/500_2024_9847_Fig1_HTML.png" 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/s00500-024-09847-0?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 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Knowl Inf Syst, 1–13</p></li></ol><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/s10115-011-0463-8?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="Acknowledgments"><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">Acknowledgments</h2><div class="c-article-section__content" id="Ack1-content"><p>We thank the anonymous reviewers for their insightful comments and the many suggestions that contributed substantially to the improvement of the document.</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">HG 7.46, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands</p><p class="c-article-author-affiliation__authors-list">Faisal Kamiran</p></li><li id="Aff2"><p class="c-article-author-affiliation__address">HG 7.82a, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands</p><p class="c-article-author-affiliation__authors-list">Toon Calders</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-Faisal-Kamiran-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Faisal Kamiran</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=Faisal%20Kamiran" class="c-article-button" data-track="click" data-track-action="author link - publication" data-track-label="link" rel="nofollow">View author publications</a></div><div class="c-article-authors-search__item c-article-authors-search__list-item--right"><p class="search-in-title-js c-article-authors-search__text">You can also search for this author in <span class="c-article-identifiers"><a class="c-article-identifiers__item" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Faisal%20Kamiran" data-track="click" data-track-action="author link - pubmed" data-track-label="link" rel="nofollow">PubMed</a><span class="u-hide"> </span><a class="c-article-identifiers__item" href="http://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Faisal%20Kamiran%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en" data-track="click" data-track-action="author link - scholar" data-track-label="link" rel="nofollow">Google Scholar</a></span></p></div></div></li><li id="auth-Toon-Calders-Aff2"><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 - publication" data-track-label="link" rel="nofollow">View author publications</a></div><div class="c-article-authors-search__item c-article-authors-search__list-item--right"><p class="search-in-title-js c-article-authors-search__text">You can also search for this author in <span class="c-article-identifiers"><a class="c-article-identifiers__item" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Toon%20Calders" data-track="click" data-track-action="author link - pubmed" data-track-label="link" rel="nofollow">PubMed</a><span class="u-hide"> </span><a class="c-article-identifiers__item" href="http://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Toon%20Calders%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en" data-track="click" data-track-action="author link - scholar" data-track-label="link" rel="nofollow">Google Scholar</a></span></p></div></div></li></ol></div><h3 class="c-article__sub-heading" id="corresponding-author">Corresponding author</h3><p id="corresponding-author-list">Correspondence to <a id="corresp-c1" href="mailto:faisal.kamiran@gmail.com">Faisal Kamiran</a>.</p></div></div></section><section data-title="Additional information"><div class="c-article-section" id="additional-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="additional-information">Additional information</h2><div class="c-article-section__content" id="additional-information-content"><p>This paper is an extended version of the papers [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. In: IEEE ICDM workshop on domain driven data mining. IEEE press" href="/article/10.1007/s10115-011-0463-8#ref-CR3" id="ref-link-section-d7811105e386">3</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 13" title="Kamiran F, Calders T (2009a) Classifying without discriminating. In: Proceedings of IEEE IC4 international conference on computer, Control & Communication. IEEE press" href="/article/10.1007/s10115-011-0463-8#ref-CR13" id="ref-link-section-d7811105e389">13</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 14" title="Kamiran F, Calders T (2009b) Discrimination-aware classification. In: BNAIC Benelux conference on artificial intelligence" href="/article/10.1007/s10115-011-0463-8#ref-CR14" id="ref-link-section-d7811105e392">14</a>].</p></div></div></section><section data-title="Rights and permissions"><div class="c-article-section" id="rightslink-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="rightslink">Rights and permissions</h2><div class="c-article-section__content" id="rightslink-content"> <p><b>Open Access</b> This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (<a href="https://creativecommons.org/licenses/by-nc/2.0" rel="license">https://creativecommons.org/licenses/by-nc/2.0</a>), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.</p> <p class="c-article-rights"><a data-track="click" data-track-action="view rights and permissions" data-track-label="link" href="https://s100.copyright.com/AppDispatchServlet?title=Data%20preprocessing%20techniques%20for%20classification%20without%20discrimination&author=Faisal%20Kamiran%20et%20al&contentID=10.1007%2Fs10115-011-0463-8&copyright=The%20Author%28s%29&publication=0219-1377&publicationDate=2011-12-03&publisherName=SpringerNature&orderBeanReset=true&oa=CC%20BY-NC">Reprints and permissions</a></p></div></div></section><section aria-labelledby="article-info" data-title="About this article"><div class="c-article-section" id="article-info-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="article-info">About this article</h2><div class="c-article-section__content" id="article-info-content"><div class="c-bibliographic-information"><div class="c-bibliographic-information__column"><h3 class="c-article__sub-heading" id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Kamiran, F., Calders, T. Data preprocessing techniques for classification without discrimination. <i>Knowl Inf Syst</i> <b>33</b>, 1–33 (2012). https://doi.org/10.1007/s10115-011-0463-8</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s10115-011-0463-8?format=refman&flavour=citation">Download citation<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><ul class="c-bibliographic-information__list" data-test="publication-history"><li class="c-bibliographic-information__list-item"><p>Received<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2010-11-23">23 November 2010</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Revised<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2011-08-23">23 August 2011</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Accepted<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2011-11-16">16 November 2011</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Published<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2011-12-03">03 December 2011</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Issue Date<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2012-10">October 2012</time></span></p></li><li class="c-bibliographic-information__list-item c-bibliographic-information__list-item--full-width"><p><abbr title="Digital Object 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