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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bias In, Bias Out? Evaluating the Folk Wisdom · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1909.08518/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1909.08518/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bias In, Bias Out? Evaluating the Folk Wisdom — mentioned by potatoman22</title><link>https://news.ycombinator.com/item?id=35171175</link><guid isPermaLink="false">urn:hackerpapers:mention:1909.08518:35171175</guid><pubDate>Wed, 15 Mar 2023 16:54:46 GMT</pubDate><description>&lt;p&gt;Your statement isn&amp;#x27;t true, there are a few scenarios where ML can reduce bias. If the human using the ML system is more biased than the average training sample, then the ML system&amp;#x27;s predictions could reduce their bias. There&amp;#x27;s also a phenomenon called bias reversal, where the ML system will be biased in the opposite direction of the humans generating the dataset. This occurs when the datasets is built through a non-random biased sampling methodology, e.g. racist police officers checking for illegal items. I&amp;#x27;m not going to go into the full details, but here&amp;#x27;s a paper on it…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1909.08518/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
