Publication Type

Journal Article

Version

submittedVersion

Publication Date

10-2025

Abstract

The governance of digitalization— which encompasses developments such as artificial intelligence (AI) and central bank digital currencies (CBDCs) — confronts serious challenges. At the core of the challenges are uncertainties, which form the central obstacle to effective governance. These uncertainties range from cyber risks to shifting societal responses. Law, as an institutionalized framework of governance, faces mounting pressure and wrestles with a fundamental vulnerability: traditional legal frameworks struggle to address uncertainties in digitalization.

To tackle the pervasive challenges of uncertainties, this article addresses two connected questions: What uncertainties does law face in governing digitalization shaped by emerging technologies? How can a learning-oriented governance approach — with law as a central component — be developed to mitigate these uncertainties? It argues that uncertainties in digitalization stem primarily from constraints of understanding: (i) imperfect knowledge, reflecting the limits of current understanding, including unknown aspects of emerging technologies and their unintended effects; (ii) incomplete knowledge, arising from fragmented expertise, information silos, and insufficient mechanisms for knowledge-sharing; and (iii) unpredictability, stemming from societal reactions to technological, (geo)economic, and regulatory developments. These interrelated constraints underlie many governance challenges in digital transformation. Addressing this “Achilles’ heel” requires a learning-oriented approach, where law works alongside adaptive institutions to manage evolving risks.

Drawing on lessons from CBDCs as a digital experiment, this article examines how governance systems can adapt to emerging uncertainties. While CBDCs have specific characteristics as national currencies, they expose structural governance vulnerabilities that recur across other digital technologies, including AI. Examples include cybersecurity vulnerabilities, data privacy risks, knowledge asymmetries, and institutional coordination gaps. Insights from CBDCs therefore inform broader strategies for addressing these constraints of understanding in AI governance, where the scale and speed of technological change magnify such constraints of understanding and test the adaptive capacity of law.

The article proposes a learning-oriented governance framework that leverages problem-solving collaborative networks to enable regulators and diverse stakeholders to co-produce knowledge, define problems, iteratively test assumptions, and build trust — particularly process-based trust — through regulatory and institutional arrangements. This forward-looking framework informs governance strategies for AI and other emerging technologies, offering practical pathways for legal systems to navigate uncertainties on the digital frontier. By embedding learning more deeply into governance, law can mitigate what appears to be an Achilles’ heel, strengthening its capacity to sustain governance at the digital frontier.

Discipline

Banking and Finance Law | Comparative and Foreign Law | Science and Technology Law

Research Areas

Corporate, Finance and Securities Law

Areas of Excellence

Digital transformation

Publication

Brooklyn Journal of Corporate, Financial and Commercial Law

Volume

20

Issue

2

First Page

1

Last Page

35

ISSN

1934-2497

Identifier

10.2139/ssrn.5686282

Publisher

Brooklyn Law School

Embargo Period

11-3-2025

Additional URL

https://doi.org/10.2139/ssrn.5686282

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