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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adversarial Removal of Demographic Attributes from Text Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1808.06640/</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/1808.06640/feed.xml" rel="self" type="application/rss+xml" /><item><title>Adversarial Removal of Demographic Attributes from Text Data — mentioned by laughingman2</title><link>https://news.ycombinator.com/item?id=17845550</link><guid isPermaLink="false">urn:hackerpapers:mention:1808.06640:17845550</guid><pubDate>Sun, 26 Aug 2018 12:02:10 GMT</pubDate><description>&lt;p&gt;Though not the same exact problem, the current machine learning research in nlp to remove unrelated bias is facing trouble in removing demographics from learnt representations. Vector representations of textual data of users encode demographics data even when they are explicitly trained no to, and the task they are trained for seems tangential to the demographic data. See this paper https://arxiv.org/abs/1808.06640 by Yanai Elazar, Yoav Goldberg. &amp;quot;Adversarial Removal of Demographic Attributes from Text Data&amp;quot; Recent advances in Representation Learning and Adversarial Training seem to succeed…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1808.06640/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
