<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1317-0570</journal-id>
<journal-title><![CDATA[Telos]]></journal-title>
<abbrev-journal-title><![CDATA[Telos]]></abbrev-journal-title>
<issn>1317-0570</issn>
<publisher>
<publisher-name><![CDATA[Universidad Dr. Rafael Belloso Chacín]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1317-05702026000100164</article-id>
<article-id pub-id-type="doi">10.36390/telos281.16</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[REGULATING ETHICAL AI: FRAMEWORKS AND BEST PRACTICES FOR FAIRNESS AND TRANSPARENCY]]></article-title>
<article-title xml:lang="es"><![CDATA[Regulación ética de IA: Marcos y buenas prácticas para la equidad y la transparencia]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Riabokon]]></surname>
<given-names><![CDATA[Viktoriia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,wiolettawwwww@gmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ukraine</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2026</year>
</pub-date>
<volume>28</volume>
<numero>1</numero>
<fpage>164</fpage>
<lpage>175</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S1317-05702026000100164&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S1317-05702026000100164&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S1317-05702026000100164&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The relevance of the study is due to the fragmented nature of the current ethical and legal regulation of AI alongside the lack of a unified model of regulatory compliance against the backdrop of trans jurisdictional risks. The purpose of this study is to substantiate the SCALEs-15 framework as an integrated model for AI regulation, grounded in the principles of equity and transparency. The research employed an array of methods, including comparative ethic-legal analysis, decomposition of normative model, metric conformity assessment, normative modeling, as well as legal forecasting. The optimized SCALEs-15 demonstrated complete coverage of target metrics, exceeding the AI Act in terms of the level of operationalization of principles and for procedural certification, which makes it expedient to legally endorse it as a unified model for the ethical and legal regulation of AI. The scientific novelty of the research lies in the substantiation of SCALEs-15 as the first normatively structured model that integrates principles of AI governance with metrics of fairness, privacy, and accountability into a procedural compliance system.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La pertinencia del estudio se debe a la naturaleza fragmentada de la actual regulación ética y jurídica de la IA, junto con la falta de un modelo unificado de cumplimiento normativo en un contexto de riesgos trans jurisdiccionales. El propósito de este estudio es fundamentar el marco SCALEs-15 como modelo integrado de regulación de la IA, basado en los principios de equidad y transparencia. La investigación empleó una serie de métodos, como el análisis ético-jurídico comparativo, la descomposición del modelo normativo, la evaluación de la conformidad métrica, el modelado normativo y la previsión jurídica. El SCALEs-15 optimizado demostró una cobertura completa de las métricas objetivo, superando la Ley de IA en cuanto al nivel de operacionalización de los principios y para la certificación procedimental, lo que hace conveniente respaldarlo legalmente como modelo unificado para la regulación ética y legal de la IA. La novedad científica de la investigación radica en la fundamentación de SCALEs-15 como el primer modelo normativamente estructurado que integra principios de gobernanza de la IA con métricas de equidad, privacidad y responsabilidad en un sistema de cumplimiento de procedimientos. Las futuras vías de investigación se centran en el modelado detallado de SCALEs-15, con el objetivo de transformar los principios en procedimientos normativos aplicables.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[AI Governance]]></kwd>
<kwd lng="en"><![CDATA[ethical compliance]]></kwd>
<kwd lng="en"><![CDATA[fairness metrics]]></kwd>
<kwd lng="en"><![CDATA[regulatory harmonization]]></kwd>
<kwd lng="en"><![CDATA[legal accountability]]></kwd>
<kwd lng="en"><![CDATA[scales-15 framework]]></kwd>
<kwd lng="en"><![CDATA[AI risk stratification]]></kwd>
<kwd lng="es"><![CDATA[Gobernanza de la IA]]></kwd>
<kwd lng="es"><![CDATA[cumplimiento ético]]></kwd>
<kwd lng="es"><![CDATA[métricas de equidad]]></kwd>
<kwd lng="es"><![CDATA[armonización normativa]]></kwd>
<kwd lng="es"><![CDATA[responsabilidad jurídica]]></kwd>
<kwd lng="es"><![CDATA[marco de escalas-15]]></kwd>
<kwd lng="es"><![CDATA[estratificación del riesgo de IA]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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