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Algunas cuestiones problemáticas en el Reglamento de Inteligencia Artificial: concepto de sistema, identificación de los sujetos obligados y delimitación de su ámbito de responsabiidad

Authors: Ricca Lopez, Guido Eduardo;

Algunas cuestiones problemáticas en el Reglamento de Inteligencia Artificial: concepto de sistema, identificación de los sujetos obligados y delimitación de su ámbito de responsabiidad

Abstract

[Resumen] El TFM examina el Reglamento de Inteligencia Artificial de la UE, focalizándose en la compleja distribución de responsabilidades entre proveedores y responsables del despliegue de sistemas de alto riesgo. Parte de un análisis contextual y de las definiciones clave—sistema de IA y modelo de uso general — para desplegar a continuación la taxonomía basada en riesgo: prácticas prohibidas, sistemas de alto riesgo, obligaciones de transparencia y usos libres. El núcleo del estudio radica en desmenuzar el catálogo de deberes que el RIA impone a cada actor—desde la alfabetización obligatoria y la evaluación ex ante de riesgos hasta la documentación técnica, la supervisión humana y la trazabilidad post-mercado—, destacando el artículo 25 como epicentro de controversias. A lo largo del trabajo, se combinan pasajes normativos con ejemplos prácticos y un análisis crítico de las lagunas y tensiones que el RIA deja abiertas: la fluctuante frontera open source, la desconexión entre gestión de riesgos y responsabilidad civil, y la rigidez del listado de sistemas de alto riesgo.

[Abstract] This Master’s Thesis provides an examination of the EU Artificial Intelligence Act,focusing on the complex allocation of responsibilities between providers and deployers of high-risk systems. It begins with a contextual analysis and key definitions—AI system and general-purpose AI model—before outlining the risk-based taxonomy: prohibited practices, high-risk systems, transparency obligations, and unrestricted uses. The core of the study unpacks the catalogue of duties the AI Act imposes on each actor—from mandatory literacy and ex ante risk assessment to technical documentation, human oversight, and post-market traceability—highlighting Article 25 as the epicenter of controversy. Throughout, the thesis weaves normative excerpts with practical examples and a critical analysis of the Act’s gaps and tensions: the shifting open-source boundary, the disconnect between risk management and civil liability, and the rigidity of the high-risk systems list.

Traballo fin de mestrado (UDC.DER). Mestrado Universitario en Dereito Dixital e da Intelixencia Artificial. Curso 2024/2025

Country
Spain
Related Organizations
Keywords

Code of Practice, responsabilidad, transparencia, Reglamento de Inteligencia Artificial, risk management, responsable del despliegue, prácticas prohibidas, open source, risk taxonomy, responsabilidad civil, regulatory compliance, open Source, artículo 25, Código de Buenas Prácticas, trazabilidad, deployer, EIDF, taxonomía de riesgos, transparency, IA, proveedor, civil liability, provider, sistemas de Alto Riesgo, high-risk systems, traceability, AI, cumplimiento normativo, Artificial Intelligence Act, article 25, prohibited practices, human oversight, Fundamental Rights Impact Assessment, liability, supervisión humana, gestión de riesgos

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green