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<oembed><version>1.0</version><provider_name>ACM SIGAI</provider_name><provider_url>https://sigai.acm.org/main</provider_url><author_name>Larry Medsker</author_name><author_url>https://sigai.acm.org/main/author/larrym/</author_url><title>AI Data - ACM SIGAI</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="qiR5xKrLGg"&gt;&lt;a href="https://sigai.acm.org/main/2020/08/27/ai-data/"&gt;AI Data&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://sigai.acm.org/main/2020/08/27/ai-data/embed/#?secret=qiR5xKrLGg" width="600" height="338" title="&#x201C;AI Data&#x201D; &#x2014; ACM SIGAI" data-secret="qiR5xKrLGg" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Confusion in the popular media about terms such as algorithm and what constitutes AI technology cause critical misunderstandings among the public and policymakers. More importantly, the role of data is often ignored in ethical and operational considerations. Even if AI systems are perfectly built, low quality and biased data cause unintentional and even intentional hazards. [&hellip;]</description></oembed>
