The Algorithmic Accountability Paradox: Rethinking Corporate Criminal Liability for Autonomous Decision-Making Systems
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Algorithmic Accountability, Artificial Intelligence, Legal Responsibility
Abstract
Background. Autonomous decision-making systems increasingly shape corporate operations, challenging criminal liability doctrines traditionally grounded in identifiable human conduct, knowledge, and culpability.
Purpose. This study examines how corporate criminal responsibility can remain legally defensible when harmful algorithmic outcomes emerge without direct human authorization.
Method. Employing qualitative doctrinal research, comparative legal analysis, and normative inquiry, the study examines United States corporate attribution principles, United Kingdom corporate offence frameworks, and European Union artificial intelligence regulation. Documentary materials are interpreted through the dimensions of attribution, culpability, causation, organizational control, and evidentiary safeguards.
Results. The analysis identifies an accountability paradox: corporations may retain substantial authority over system selection, deployment, monitoring, and intervention while lacking direct knowledge of particular algorithmic decisions. Comparative findings distinguish criminal attribution from preventive regulatory compliance and show why technological opacity neither automatically defeats liability nor establishes corporate fault.
Conclusion. The framework offers a principled basis for future legislative clarification and judicial assessment while preserving legality, proportionality, and procedural fairness in increasingly automated corporate environments. Its conclusions remain provisional pending empirical and judicial validation.
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