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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1806.07552/</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/1806.07552/feed.xml" rel="self" type="application/rss+xml" /><item><title>Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems — mentioned by 1_over_n</title><link>https://news.ycombinator.com/item?id=21958517</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.07552:21958517</guid><pubDate>Sat, 04 Jan 2020 23:48:37 GMT</pubDate><description>&lt;p&gt;This is a great point. There is a general lack of understanding about what it means for models to be interpretable &amp;amp; explainable. These words get thrown around often by people who don&amp;#x27;t understand the definition, and also the trade off with accuracy. Some papers i found interesting on the subject: https://arxiv.org/abs/1606.03490 https://arxiv.org/abs/1707.03886 https://arxiv.org/abs/1806.07552 https://arxiv.org/abs/1702.08608 (i found this was a good sumary of the issues)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.07552/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
