Wendell Wallach and Gary Marchant’s “An Agile Ethical/Legal Model for the International and National Governance of AI and Robotics” argues for a governance structure that can respond to AI and robotics faster than ordinary lawmaking usually can.

The core proposal is a Governance Coordinating Committee, or GCC, that would help coordinate ethical, legal, technical, and institutional responses to AI and robotics. The paper is not simply asking for another principles document. It is trying to describe an institutional mechanism for turning soft law into something more durable.

AI governance already has many declarations, principles, and high-level frameworks. The problem is not a shortage of ethical vocabulary. The problem is that fast-moving technology often outruns the institutions that are supposed to discipline it.

The paper’s strongest argument is that AI and robotics governance cannot be left entirely to one actor.

Governments move slowly and are constrained by jurisdiction. Engineers understand technical feasibility but do not always have authority or understanding of the consequences. Ethicists can clarify values and harms, but ethical analysis alone does not create compliance. Courts need framework in terms of laws, rules and precedents to make a judgment.

The proposed GCC is meant to sit across these groups and coordinate governance through multiple channels:

  • Government regulation
  • Engineering standards
  • Ethical guidance
  • Insurance requirements
  • Academic publication norms
  • Grant funding conditions
  • Judicial interpretation
  • Organizational compliance programs

That is a realistic way to think about AI governance. In practice, behavior changes when incentives converge from multiple directions. If insurers, journals, funders, regulators, and courts all begin expecting similar safety and transparency practices, companies have a stronger reason to treat those practices as real requirements instead of optional ethics theater.

Soft law is often criticized because it lacks direct enforcement. That criticism is fair, but then why does it matter?

In fast-moving areas such as AI, soft law can do work that formal law cannot do quickly enough. It can establish norms, define expected practice, create audit vocabulary, and give courts or regulators a reference point when formal disputes arise.

The paper’s useful insight is that soft law becomes stronger when institutions coordinate around it. A guideline from one professional group is easy to ignore. The same guideline becomes harder to ignore if it affects insurance, publication, procurement, grant funding, and litigation risk.

This is especially relevant for algorithmic transparency. Some AI systems may be explainable enough for ordinary review. Others may be opaque in ways that require different safeguards. A governance body could help define when opacity is acceptable, what testing is required before deployment, and which contexts should reject opaque systems entirely.

I agree with the paper’s broad direction. AI and robotics need governance that is international, adaptive, and institutionally connected.

The GDPR is a useful comparison. It gives enforceable rights within the European Union and has shaped global corporate behavior beyond Europe. But AI systems are not always contained within one legal jurisdiction. Models, data, users, vendors, and infrastructure can be spread across countries. A coordinating body could help reduce the gap between regional laws and global deployment.

The paper is also right that engineering and ethics need to be connected. Ethical principles are weak if they never become technical requirements. Engineering practices are dangerous if they optimize only for capability and ignore rights, safety, and human consequences.

However, the proposal leaves hard implementation questions open.

The biggest question is authority. Would the GCC merely recommend standards, or would it have some route to enforcement? If enforcement depends on other institutions, how would those institutions be coordinated? How would the GCC avoid becoming another advisory body whose output companies cite without changing behavior?

There are also questions about legitimacy:

  • Who appoints the GCC?
  • How are affected communities represented?
  • How does it avoid capture by large technology companies?
  • How does it handle disagreement between countries with different values and legal systems?
  • How does it connect to existing laws such as GDPR?
  • How would it incorporate human rights declarations such as the Toronto Declaration into operational requirements?
  • How GCC will operate internationally?
  • How will GCC attain autonomy and at the same time have authority delegated by the government of different nations?

These questions do not weaken the need for governance. They show where the real design work begins.

“An Agile Ethical/Legal Model for the International and National Governance of AI and Robotics” is persuasive because it recognizes that AI governance is not a single lever. It is a network of institutions.

The paper’s GCC proposal may or may not be the exact institution the world needs, but the underlying point is sound: AI principles require coordinated enforcement pathways. Without that, responsible AI stays mostly a matter of language.

Writing down what AI should respect is the easy part. Now, the harder work will be to build the institutions that make those commitments difficult to ignore.

Reference

Wendell Wallach and Gary E. Marchant, “An Agile Ethical/Legal Model for the International and National Governance of AI and Robotics”, AIES 2018.