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<title>Algorithmic transparency</title>
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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Algorithmic transparency</span></span>
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<div id="mw-content-text" class="mw-body-content mw-content-ltr" lang="en" dir="ltr"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><p><b>Algorithmic transparency</b> is the principle that the factors that influence the decisions made by <a href="Algorithms" class="mw-redirect" title="Algorithms">algorithms</a> should be visible, or transparent, to the people who use, regulate, and are affected by systems that employ those algorithms. Although the phrase was coined in 2016 by Nicholas Diakopoulos and Michael Koliska about the role of algorithms in deciding the content of digital journalism services,<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> the underlying principle dates back to the 1970s and the rise of automated systems for scoring consumer credit.
</p><p>The phrases "algorithmic transparency" and "algorithmic accountability"<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> are sometimes used interchangeably – especially since they were coined by the same people – but they have subtly different meanings. Specifically, "algorithmic transparency" states that the inputs to the algorithm and the algorithm's use itself must be known, but they need not be fair. "<a href="Algorithmic_accountability" title="Algorithmic accountability">Algorithmic accountability</a>" implies that the organizations that use algorithms must be accountable for the decisions made by those algorithms, even though the decisions are being made by a machine, and not by a human being.<sup id="cite_ref-Dickey_3-0" class="reference"><a href="#cite_note-Dickey-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup>
</p><p>Current research around algorithmic transparency interested in both societal effects of accessing remote services running algorithms.,<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> as well as mathematical and computer science approaches that can be used to achieve algorithmic transparency<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> In the United States, the <a href="Federal_Trade_Commission" title="Federal Trade Commission">Federal Trade Commission</a>'s Bureau of Consumer Protection studies how algorithms are used by consumers by conducting its own research on algorithmic transparency and by funding external research.<sup id="cite_ref-Noyes_7-0" class="reference"><a href="#cite_note-Noyes-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> In the <a href="European_Union" title="European Union">European Union</a>, the data protection laws that came into effect in May 2018 include a "right to explanation" of decisions made by algorithms, though it is unclear what this means.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> Furthermore, the European Union founded The European Center for Algorithmic Transparency (ECAT).<sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup>
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<div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2></div>
<ul><li><a href="Black_box" title="Black box">Black box</a></li>
<li><a href="Explainable_AI" class="mw-redirect" title="Explainable AI">Explainable AI</a></li>
<li><a href="Regulation_of_algorithms" title="Regulation of algorithms">Regulation of algorithms</a></li>
<li><a href="Reverse_engineering" title="Reverse engineering">Reverse engineering</a></li>
<li><a href="Right_to_explanation" title="Right to explanation">Right to explanation</a></li>
<li><a href="Algorithmic_accountability" title="Algorithmic accountability">Algorithmic accountability</a></li></ul>
<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>
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<li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text">Nicholas Diakopoulos & Michael Koliska (2016): Algorithmic Transparency in the News Media, Digital Journalism, <style data-mw-deduplicate="TemplateStyles:r1238218222">
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</style><a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1080%2F21670811.2016.1208053">10.1080/21670811.2016.1208053</a></span>
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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><cite id="CITEREFDiakopoulos2015" class="citation journal cs1">Diakopoulos, Nicholas (2015). <span class="id-lock-subscription" title="Paid subscription required"><a rel="nofollow" class="external text" href="https://www.cjr.org/tow_center_reports/algorithmic_accountability_on_the_investigation_of_black_boxes.php">"Algorithmic Accountability: Journalistic Investigation of Computational Power Structures"</a></span>. <i>Digital Journalism</i>. <b>3</b> (3): <span class="nowrap">398–</span>415. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1080%2F21670811.2014.976411">10.1080/21670811.2014.976411</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:42357142">42357142</a>.</cite></span>
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<li id="cite_note-Dickey-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-Dickey_3-0">^</a></b></span> <span class="reference-text"><cite id="CITEREFDickey2017" class="citation news cs1">Dickey, Megan Rose (30 April 2017). <a rel="nofollow" class="external text" href="https://techcrunch.com/2017/04/30/algorithmic-accountability/">"Algorithmic Accountability"</a>. <i>TechCrunch</i><span class="reference-accessdate">. Retrieved <span class="nowrap">4 September</span> 2017</span>.</cite></span>
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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="http://datworkshop.org/">"Workshop on Data and Algorithmic Transparency"</a>. 2015<span class="reference-accessdate">. Retrieved <span class="nowrap">4 January</span> 2017</span>.</cite></span>
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<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="http://www.fatml.org/">"Fairness, Accountability, and Transparency in Machine Learning"</a>. 2015<span class="reference-accessdate">. Retrieved <span class="nowrap">29 May</span> 2017</span>.</cite></span>
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<li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text"><cite id="CITEREFOttDabrock2022" class="citation journal cs1">Ott, Tabea; Dabrock, Peter (2022-08-22). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444183">"Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies"</a>. <i>Frontiers in Genetics</i>. <b>13</b>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.3389%2Ffgene.2022.902960">10.3389/fgene.2022.902960</a></span>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1664-8021">1664-8021</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444183">9444183</a></span>.</cite></span>
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<li id="cite_note-Noyes-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-Noyes_7-0">^</a></b></span> <span class="reference-text"><cite id="CITEREFNoyes2015" class="citation news cs1">Noyes, Katherine (9 April 2015). <a rel="nofollow" class="external text" href="http://www.pcworld.com/article/2908372/the-ftc-is-worried-about-algorithmic-transparency-and-you-should-be-too.html">"The FTC is worried about algorithmic transparency, and you should be too"</a>. <i>PCWorld</i><span class="reference-accessdate">. Retrieved <span class="nowrap">4 September</span> 2017</span>.</cite></span>
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<li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text"><cite class="citation journal cs1"><a rel="nofollow" class="external text" href="https://media.nature.com/original/magazine-assets/d41586-018-05285-9/d41586-018-05285-9.pdf">"False Testimony"</a> <span class="cs1-format">(PDF)</span>. <i>Nature</i>. <b>557</b> (7707): 612. 31 May 2018.</cite></span>
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<li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://algorithmic-transparency.ec.europa.eu/about_en">"About - European Commission"</a>.</cite></span>
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