<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Xai on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/xai/</link><description>Recent content in Xai on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/xai/index.xml" rel="self" type="application/rss+xml"/><item><title>Right to explanation</title><link>https://terms-en.ai-term-hub.com/en/terms/right_to_explanation/</link><pubDate>Sat, 18 Jul 2026 10:14:22 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/right_to_explanation/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>The right to explanation is a core component of algorithmic accountability, particularly within frameworks like the GDPR. It ensures that when an AI system makes a decision impacting a person&amp;rsquo;s rights or opportunities, such as loan denial or hiring rejection, the individual can understand the logic behind it. This transparency helps prevent discrimination, allows for effective appeals, and builds trust in automated systems by demystifying &amp;lsquo;black box&amp;rsquo; outcomes.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>A legal and ethical principle granting individuals the right to receive meaningful information about automated decisions that significantly affect them.&lt;/p></description></item><item><title>Argumentation framework</title><link>https://terms-en.ai-term-hub.com/en/terms/argumentation_framework/</link><pubDate>Sat, 18 Jul 2026 09:45:50 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/argumentation_framework/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Argumentation frameworks provide a mathematical basis for representing arguments, attacks, and defenses among them. In AI engineering, they help systems make transparent, justifiable decisions by weighing evidence for and against specific outcomes. This approach enhances explainability and trust, allowing stakeholders to understand the reasoning behind automated choices, especially in high-stakes domains like legal or medical decision support.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>A formal structure used to model and resolve conflicts between competing claims or decisions in AI systems.&lt;/p></description></item></channel></rss>