Position Paper: If Innovation in AI Systematically Violates Fundamental Rights, Is It Innovation at All?

Fuente: arXiv
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Autores principales: Castañeira, Josu Eguiluz, Brando, Axel, Laukyte, Migle, Serra-Vidal, Marc
Formato: Preprint
Publicado: 2025
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author Castañeira, Josu Eguiluz
Brando, Axel
Laukyte, Migle
Serra-Vidal, Marc
author_facet Castañeira, Josu Eguiluz
Brando, Axel
Laukyte, Migle
Serra-Vidal, Marc
contents Artificial intelligence (AI) now permeates critical infrastructures and decision-making systems where failures produce social, economic, and democratic harm. This position paper challenges the entrenched belief that regulation and innovation are opposites. As evidenced by analogies from aviation, pharmaceuticals, and welfare systems and recent cases of synthetic misinformation, bias and unaccountable decision-making, the absence of well-designed regulation has already created immeasurable damage. Regulation, when thoughtful and adaptive, is not a brake on innovation -- it is its foundation. The present position paper examines the EU AI Act as a model of risk-based, responsibility-driven regulation that addresses the Collingridge Dilemma: acting early enough to prevent harm, yet flexibly enough to sustain innovation. Its adaptive mechanisms -- regulatory sandboxes, small and medium enterprises (SMEs) support, real-world testing, fundamental rights impact assessment (FRIA) -- demonstrate how regulation can accelerate responsibly, rather than delay, technological progress. The position paper summarises how governance tools transform perceived burdens into tangible advantages: legal certainty, consumer trust, and ethical competitiveness. Ultimately, the paper reframes progress: innovation and regulation advance together. By embedding transparency, impact assessments, accountability, and AI literacy into design and deployment, the EU framework defines what responsible innovation truly means -- technological ambition disciplined by democratic values and fundamental rights.
format Preprint
id arxiv_https___arxiv_org_abs_2511_00027
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Position Paper: If Innovation in AI Systematically Violates Fundamental Rights, Is It Innovation at All?
Castañeira, Josu Eguiluz
Brando, Axel
Laukyte, Migle
Serra-Vidal, Marc
Computers and Society
Artificial Intelligence
Machine Learning
Applications
Artificial intelligence (AI) now permeates critical infrastructures and decision-making systems where failures produce social, economic, and democratic harm. This position paper challenges the entrenched belief that regulation and innovation are opposites. As evidenced by analogies from aviation, pharmaceuticals, and welfare systems and recent cases of synthetic misinformation, bias and unaccountable decision-making, the absence of well-designed regulation has already created immeasurable damage. Regulation, when thoughtful and adaptive, is not a brake on innovation -- it is its foundation. The present position paper examines the EU AI Act as a model of risk-based, responsibility-driven regulation that addresses the Collingridge Dilemma: acting early enough to prevent harm, yet flexibly enough to sustain innovation. Its adaptive mechanisms -- regulatory sandboxes, small and medium enterprises (SMEs) support, real-world testing, fundamental rights impact assessment (FRIA) -- demonstrate how regulation can accelerate responsibly, rather than delay, technological progress. The position paper summarises how governance tools transform perceived burdens into tangible advantages: legal certainty, consumer trust, and ethical competitiveness. Ultimately, the paper reframes progress: innovation and regulation advance together. By embedding transparency, impact assessments, accountability, and AI literacy into design and deployment, the EU framework defines what responsible innovation truly means -- technological ambition disciplined by democratic values and fundamental rights.
title Position Paper: If Innovation in AI Systematically Violates Fundamental Rights, Is It Innovation at All?
topic Computers and Society
Artificial Intelligence
Machine Learning
Applications
url https://arxiv.org/abs/2511.00027