AI, Complexity, and Regulation

OUP Handbook on AI Governance, Forthcoming

22 Pages Posted: 13 Dec 2021

See all articles by Laurin Weissinger

Laurin Weissinger

Tufts University - The Fletcher School of Law and Diplomacy

Date Written: October 14, 2021


Regulating and governing AI will remain a challenge due to the inherent intricacy of how AI is deployed and used in practice. Regulation effectiveness and efficiency is inversely proportional to system complexity and the clarity of objectives: the more complicated an area is and the harder objectives are to operationalize, the more difficult it is to regulate and govern. Safety regulations, while often concerned with complex systems like airplanes, benefit from measurable, clear objectives and uniform subsystems. AI has emergent properties, and is not just “a technology” but interwoven with organizations, people, and the wider social context. Furthermore, objectives like “fairness” are not only difficult to grasp and classify but they will change their meaning case-by-case.

The inherent complexity of AI systems will continue to complicate regulation and governance but with appropriate investment, monetary and other- wise, complexity can be tackled successfully. However, due to the consider- able power imbalance between users of AI in comparison to those AI systems are used on, successful regulation might be difficult to create and enforce. As such, AI regulation is more of a political and socio-economic problem than a technical one.

Keywords: Artificial Intelligence, AI, Machine Learning, Complexity, Regulation, Governance

Suggested Citation

Weissinger, Laurin, AI, Complexity, and Regulation (October 14, 2021). OUP Handbook on AI Governance, Forthcoming , Available at SSRN:

Laurin Weissinger (Contact Author)

Tufts University - The Fletcher School of Law and Diplomacy

160 Packard
Medford, MA 02155
United States

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