Research Focus

Governance & Regulation

Legal frameworks, organizational policies and standards for responsible AI

Why does governance matter?

Technology without regulation produces externalities. Governments, companies and universities need frameworks that ensure AI is developed responsibly. But poorly designed regulation stifles innovation. Our work seeks balance.

We study emerging laws (EU AI Act), ISO technical standards, corporate ethical frameworks and public policies to identify how best to implement trustworthiness at scale.

Regulatory Frameworks

Emerging norms guiding responsible development

Europa

EU AI Act

Pioneer European law that classifies AI by risk, requires documentation and audit. Sets de facto global standards.

Estándares

ISO/IEC 42001

International standard for AI management systems. Defines audit requirements, risk and compliance.

EEUU

Executive Order

US executive order on safe AI, with emphasis on transparency and consumer protection.

Corporativo

Internal Ethical Frameworks

Corporate policies for responsible AI. Ethics committees, internal audits and principles aligned with values.

Pillars of Effective Governance

Key components of a governance framework

Documentary Transparency

Document design decisions, risk assessments, testing, validation. Complete traceability.

Audit and Certification

Independent third parties verify compliance. Certification of the AI management system.

Multi-sectoral Governance

Collaboration between industry, government, academia and civil society to establish accepted standards.

Regulatory Challenges

Obstacles in harmonization and compliance

Global Fragmentation

Different countries regulate differently. Multinational companies face regulatory conflicts. Need for harmonization.

Rapidly Evolving Technology

Regulation is slow, AI is fast. Legal frameworks can become obsolete. Need for regulatory flexibility.

Cost of Compliance

Audits, documentation, certification. Especially burdensome for startups and small actors.

Practical Applications

Scenarios where governance and regulatory frameworks make the difference

AI Act Compliance: Guiding organizations through the European AI Act risk-based classification, documentation requirements, and conformity assessment procedures.
Algorithmic Auditing: Implementing structured audits for high-risk AI systems to verify transparency, traceability, and human oversight before deployment.
Public Sector Procurement: Establishing criteria and checklists for public administrations to procure AI systems that meet ethical and legal standards.
Corporate Governance: Designing internal policies, ethics boards, and accountability mechanisms for responsible AI development within companies.
Cross-Border Deployment: Navigating conflicting regulatory landscapes when deploying AI systems across multiple jurisdictions with different legal requirements.
Incident Response: Creating protocols for detecting, reporting, and remediating AI system failures or harms in compliance with emerging liability frameworks.

Our Research

In the AI Governance and Regulation line of the Trustworthy AI group at IAFER, we research audit frameworks, documentation, and governance aligned with European AI regulations.

🚀 Active Research Lines

AI Management Systems: Frameworks to document, audit and certify the lifecycle of AI systems
Regulatory Compliance Assessment: Methods to verify adherence to European regulations like the AI Act
Transparency and Traceability: Techniques to ensure comprehensive documentation of decisions and changes in AI systems
Algorithmic Audit: Processes to verify impact, bias and risks in deployed AI systems

📚 Publicaciones

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