<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>EXplainable Artificial Intelligence (XAI) on Jannik P. Roth</title><link>https://jannik-roth.github.io/tags/explainable-artificial-intelligence-xai/</link><description>Recent content in EXplainable Artificial Intelligence (XAI) on Jannik P. Roth</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Tue, 21 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jannik-roth.github.io/tags/explainable-artificial-intelligence-xai/index.xml" rel="self" type="application/rss+xml"/><item><title>Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values</title><link>https://jannik-roth.github.io/papers/2026_fragment_shapley/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://jannik-roth.github.io/papers/2026_fragment_shapley/</guid><description>This work introduces a fragment-level Shapley value framework that enables the exact computation of feature contributions at the level of chemically meaningful fragments for molecular property predictions without relying on sampling or feature imputation. Preprint in ChemRxiv, 2026</description></item></channel></rss>