<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Continuous Compliance on Formize.com Blog</title><link>https://blog.formize.com/tags/continuous-compliance/</link><description>Recent content in Continuous Compliance on Formize.com Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.formize.com/tags/continuous-compliance/index.xml" rel="self" type="application/rss+xml"/><item><title>Accelerating Responsible AI Model Card Creation with Formize</title><link>https://blog.formize.com/accelerating-responsible-ai-model-card-creation-with-formize/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/accelerating-responsible-ai-model-card-creation-with-formize/</guid><description>&lt;h1 id="accelerating-responsible-ai-model-card-creation-with-formize">Accelerating Responsible AI Model Card Creation with Formize&lt;/h1>
&lt;p>Artificial intelligence models are increasingly being deployed in high‑stakes domains—healthcare, finance, autonomous systems, and content generation. Regulators, auditors, and internal ethics boards now demand transparent documentation that explains a model’s purpose, data provenance, performance metrics, fairness assessments, and risk mitigations. The &lt;strong>model card&lt;/strong> has become the de‑facto standard for this documentation, but creating and maintaining model cards at scale remains a manual, error‑prone process.&lt;/p></description></item></channel></rss>