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New mitigation framework reduces bias in classification outcomes - EurekAlert

<p>We use computers to help us make (hopefully) unbiased decisions. The problem is that machine-learning algorithms do not always make fair classifications, if human bias is embedded in the data used to train them &mdash; which is often the case in practice. To ease this &quot;garbage in, garbage out&quot; situation, a research team presented a flexible framework for mitigating bias in machine classification. Their research was published Apr. 8 in<em> </em><em>Intelligent Computing</em>, a Science Partner Journal.</p>

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