VERITAS: Verifiable Evaluation and Reporting In Transparent AI Systems

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Main Author: Gerald Enrique Nelson Mc Kenzie
Format: Recurso digital
Published: Zenodo 2025
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author Gerald Enrique Nelson Mc Kenzie
author_facet Gerald Enrique Nelson Mc Kenzie
contents <p><span><span>In this work, </span><span>I</span><span> present VERITAS, a self-introspective engine designed to autonomously generate comprehensive model cards for machine learning systems. VERITAS integrates performance monitoring, explainable AI, bias auditing, and compliance logging into a unified framework. By enabling models to generate their own “model cards” VERITAS enhances transparency, accountability, and trustworthiness in AI deployments. </span><span>T</span><span>he Iris dataset</span><span> is used for validation</span><span>, using a </span><span>RandomForestClassifier</span><span>, and </span><span>demonstrates</span><span> that VERITAS produces verifiable, detailed reports that capture the model’s capabilities, limitations, and operational metrics.</span></span><span> </span></p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_14811299
institution Zenodo
language
publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle VERITAS: Verifiable Evaluation and Reporting In Transparent AI Systems
Gerald Enrique Nelson Mc Kenzie
<p><span><span>In this work, </span><span>I</span><span> present VERITAS, a self-introspective engine designed to autonomously generate comprehensive model cards for machine learning systems. VERITAS integrates performance monitoring, explainable AI, bias auditing, and compliance logging into a unified framework. By enabling models to generate their own “model cards” VERITAS enhances transparency, accountability, and trustworthiness in AI deployments. </span><span>T</span><span>he Iris dataset</span><span> is used for validation</span><span>, using a </span><span>RandomForestClassifier</span><span>, and </span><span>demonstrates</span><span> that VERITAS produces verifiable, detailed reports that capture the model’s capabilities, limitations, and operational metrics.</span></span><span> </span></p>
title VERITAS: Verifiable Evaluation and Reporting In Transparent AI Systems
url https://doi.org/10.5281/zenodo.14811299