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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2106.02605/</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/2106.02605/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations — mentioned by qPM9l3XJrF</title><link>https://news.ycombinator.com/item?id=28838321</link><guid isPermaLink="false">urn:hackerpapers:mention:2106.02605:28838321</guid><pubDate>Tue, 12 Oct 2021 11:09:58 GMT</pubDate><description>&lt;p&gt;Of the &amp;quot;representative publications&amp;quot; on her homepage here https://ece.duke.edu/faculty/cynthia-rudin the one that seems most relevant to the topic of the article is this paper on interpretable financial lending: https://arxiv.org/pdf/2106.02605.pdf &amp;quot;The machine learning model is a two-layer additive risk model, which resembles a two-layer neural network, but is decomposable into subscales. In this model, each node in the first (hidden) layer represents a meaningful subscale model, and all of the nonlinearities are transparent. Our online visualization tool allows exploration of this model…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2106.02605/"&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>
