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<titleInfo><title>Fairness through regularization for learning to rank</title></titleInfo>


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<name type="personal">
  <namePart type="given">Nikola H</namePart>
  <namePart type="family">Konstantinov</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">4B9D76E4-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0009-0009-5204-7621</description></name>
<name type="personal">
  <namePart type="given">Christoph</namePart>
  <namePart type="family">Lampert</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">40C20FD2-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-4561-241X</description></name>







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<abstract lang="eng">Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality.</abstract>

<originInfo><dateIssued encoding="w3cdtf">2021</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>arXiv</title></titleInfo>
  <identifier type="arXiv">2102.05996</identifier><identifier type="doi">10.48550/arXiv.2102.05996</identifier>
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  <location>     <url>https://research-explorer.ista.ac.at/record/10799</url>  </location>
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<chicago>Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” &lt;i&gt;ArXiv&lt;/i&gt;, n.d. &lt;a href=&quot;https://doi.org/10.48550/arXiv.2102.05996&quot;&gt;https://doi.org/10.48550/arXiv.2102.05996&lt;/a&gt;.</chicago>
<ista>Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996.</ista>
<mla>Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.” &lt;i&gt;ArXiv&lt;/i&gt;, 2102.05996, doi:&lt;a href=&quot;https://doi.org/10.48550/arXiv.2102.05996&quot;&gt;10.48550/arXiv.2102.05996&lt;/a&gt;.</mla>
<apa>Konstantinov, N. H., &amp;#38; Lampert, C. (n.d.). Fairness through regularization for learning to rank. &lt;i&gt;arXiv&lt;/i&gt;. &lt;a href=&quot;https://doi.org/10.48550/arXiv.2102.05996&quot;&gt;https://doi.org/10.48550/arXiv.2102.05996&lt;/a&gt;</apa>
<short>N.H. Konstantinov, C. Lampert, ArXiv (n.d.).</short>
<ama>Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. &lt;i&gt;arXiv&lt;/i&gt;. doi:&lt;a href=&quot;https://doi.org/10.48550/arXiv.2102.05996&quot;&gt;10.48550/arXiv.2102.05996&lt;/a&gt;</ama>
<ieee>N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” &lt;i&gt;arXiv&lt;/i&gt;. .</ieee>
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