Governing the Ungovernable? Corporate accountability, board roles, and organizational responsibility in the age of (artificial) intelligence

Éditeurs invités

• Kim Ceulemans, TBS Business School
• Lotfi Karoui, ISC Paris
• Agota Szabo, VU Amsterdam
• Wafa Khlif, TBS Business School

Synthèse

Organizations increasingly rely on AI for consequential decisions across hiring, credit, and compliance. Yet governance frameworks designed for human-centered processes struggle to address distributed, opaque systems operating at unprecedented scale and speed. Traditional oversight mechanisms—boards, audit committees, regulators—were not built to govern technologies that challenge assumptions about accountability and responsibility.

This special issue examines how corporate governance frameworks can evolve to address AI's organizational implications. Rather than treating governance as purely technical, the issue emphasizes that governance shapes whose interests matter and how accountability is defined. The call seeks research exploring the mismatch between rapid, complex AI-enabled decision-making and slower institutional oversight.

Extrait. Lire l'appel complet sur le site de l'éditeur.

Thèmes proposés

• Board oversight of AI, algorithmic risk, and digital infrastructures
• Fiduciary duty, corporate purpose, and stakeholder accountability in AI-intensive firms
• AI, sustainability, and the governance of social and environmental impacts
• Algorithmic management, the organization of labour, and workplace surveillance
• Audit, assurance, internal control, and the challenge of model opacity
• AI regulation, soft law, and the organizational translation of legal requirements
• Data governance, data ownership, and cross-border accountability
• Bias, discrimination, and issues related to classification, prediction, and fairness in AI systems
• Human oversight, contestability, and the limits of explainability
• AI in public administration, welfare systems, healthcare, policing, and education
• The role of AI in sensitive or high-impact contexts, including security, defense, and critical infrastructures
• Alternative and plural governance models for responsible AI
• Stakeholder participation, oversight, and engagement in AI governance