Gender biased algorithmic hiring

<p>Building on <a class="wikilink" href="/the_myth_of_the_double_blind/">the myth of the double blind</a>, Criado Perez<sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup> tackles the next big issue for "artificial intelligence", which is the <a class="wikilink" href="/recruitment_bias/">recruitment bias</a>. Although it could be possible to overcome the limitations (and biases) of CV-screening by a human, algorithms which are mainly done by <em>men</em> will fall pray of their own limitations. </p> <p>For example, a company call Gild, was using some indicators of programming <em>excellence</em> of candidates. Those indicators included how often people were engaging on a specific manga website. This, however, does not account for the fact that women may not have the time for such banalities (see: <a class="wikilink" href="/how_much_unpaid_work_is_done_by_women/">how much unpaid work is done by women</a>), or may be penalized for such an anti-social behavior (see: <a class="wikilink" href="/expected_female_traits/">expected female traits</a>). </p> <p>Therefore, Gild's system <em>is</em> biased towards men but they hide behind secrecy and the absurd notion of the purity of algorithms (see: <a class="wikilink" href="/what_are_algorithms/">what are algorithms</a>). </p> <p>Tags: <a href="/tags/gender-bias">#gender-bias</a> <a href="/tags/gender-bias-in-business">#gender-bias-in-business</a> <a href="/tags/gender-bias-in-ai">#gender-bias-in-ai</a></p> <div class="footnote"> <hr /> <ol> <li id="fn:1"> <p><a class="wikilink" href="/invisible_women_-_caroline_criado_perez/">Invisible Women - Caroline Criado Perez</a>&#160;<a class="footnote-backref" href="#fnref:1" title="Jump back to footnote 1 in the text">&#8617;</a></p> </li> </ol> </div>