<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Trust on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/trust/</link><description>Recent content in Trust 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/trust/index.xml" rel="self" type="application/rss+xml"/><item><title>Transparency</title><link>https://terms-en.ai-term-hub.com/en/terms/transparency/</link><pubDate>Sat, 18 Jul 2026 09:43:02 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/transparency/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Transparency ensures that stakeholders can understand how an AI model arrives at its outputs, fostering trust and accountability. It involves disclosing training data origins, model architectures, and potential biases. In ethical AI frameworks, transparency complements explainability by making system behaviors predictable and auditable, allowing users to verify fairness and identify errors without requiring deep technical expertise.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>The degree to which an AI system&amp;rsquo;s decision-making processes, data sources, and limitations are open and understandable to users.&lt;/p></description></item></channel></rss>