<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainable AI on Formize.com Blog</title><link>https://blog.formize.com/categories/explainable-ai/</link><description>Recent content in Explainable AI on Formize.com Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.formize.com/categories/explainable-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Bridging Explainable AI and Synthetic Data Governance with Formize</title><link>https://blog.formize.com/explainable-ai-and-synthetic-data-governance-with-formize/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/explainable-ai-and-synthetic-data-governance-with-formize/</guid><description>&lt;h1 id="bridging-explainable-ai-and-synthetic-data-governance-with-formize">Bridging Explainable AI and Synthetic Data Governance with Formize&lt;/h1>
&lt;p>Artificial intelligence is moving from experimental labs into mission‑critical production environments. Two trends dominate this shift:&lt;/p>
&lt;ol>
&lt;li>&lt;strong>Synthetic data&lt;/strong> – generated to protect privacy, accelerate model training, and enrich scarce datasets.&lt;/li>
&lt;li>&lt;strong>Explainable AI (XAI)&lt;/strong> – required by regulators, auditors, and end‑users who demand to understand &lt;em>why&lt;/em> a model makes a particular prediction.&lt;/li>
&lt;/ol>
&lt;p>While both topics have mature toolsets, they are often treated as silos. Synthetic data pipelines generate data, and XAI tools explain model behavior, but there is rarely a single source of truth that ties the two together. This gap creates compliance risk, hampers auditability, and erodes stakeholder trust.&lt;/p></description></item></channel></rss>