Inquiry is half of the field. These questions remain unsettled; each position is attributed to its sources, and each debate is tagged with the objects and tests it turns on.
Question under dispute
Risk management or rights governance?
Should governance begin from managed uncertainty or from duties that cannot be traded away?
Why it matters. The starting point changes who defines harm, which impacts can be accepted, and what counts as an adequate response.
Risk-centered
Contextual risk processes help organizations prioritize, measure, and manage diverse impacts over time.
Rights-centered
Human dignity and rights set normative boundaries for what institutions and organizations may do.
Layered
Rights establish constraints and entitlements; risk methods support operational decisions within those boundaries.
Question under dispute
Voluntary practice or binding rule?
When does guidance become an obligation, and what is lost if those forms are treated as interchangeable?
Why it matters. Authority, enforcement, evidence, and remedy differ even when two instruments use similar language, and only some forms change incentives.
Practice-led
Voluntary frameworks can move faster, fit diverse organizations, and improve internal capability.
Law-led
Binding duties create minimum floors, public supervision, and consequences that internal practice alone cannot provide.
Question under dispute
Compute controls: safety lever or power consolidator?
Should governments govern AI through control of advanced compute, given that the same lever concentrates power?
Why it matters. Compute is the most governable input, but a control that only the largest actors can satisfy changes who gets to build, and for how long.
Effective intervention point
Compute is detectable, excludable, and quantifiable, so it offers regulatory visibility and enforcement unavailable elsewhere, provided guardrails limit the risks.
Concentration and dependence risk
Compute controls can centralize power, entrench incumbents, and deepen dependence for countries and communities already excluded from the inputs.
Floor before ceiling
Before restricting access at the top, put a floor under the AI divide through shared compute, data, and capacity, since universal frontier access is unrealistic.
Question under dispute
Govern now, or wait for evidence?
When should governance intervene, given that early action is poorly informed and late action is costly?
Why it matters. Both horns of the dilemma produce lock-in: acting late hardens technology and markets, acting rigidly hardens the wrong rules.
Act early: the window closes
Change is cheapest before adoption compounds; once arrangements are embedded, reversal is costly even when consequences are clear.
Wait: rules written in ignorance lock in
Early rules are written with the least information, and the categories they create acquire defenders of their own.
Anticipatory and adaptive: act early, specify review
Build foresight, engagement, and revision into governance so it can act while management is still possible and change when assumptions fail.
Question under dispute
Who should set the rules for a borderless technology?
Should international AI governance be built through coalitions of leading AI nations, universal institutions, or binding regional law?
Why it matters. Representation decides whose priorities enter the agenda: seven countries are party to all of seven sampled initiatives and 118 to none.
Plurilateral initiatives
Coalitions of capable states move faster and experiment with norms; the UN advisory body calls selectivity understandable at an early stage while warning it must not persist.
Universal, UN-anchored governance
A borderless technology needs governance with global reach, anchored in international law and inclusive of the countries currently absent.
Binding regional and treaty law
Enforceable rules with real authorities, whether a regional regulation or a treaty open to non-members, do governance work that declarations cannot.
Question under dispute
Should AI companions be available to minors?
Should young people be able to use AI companion chatbots, and on what terms?
Why it matters. Teenagers already use chatbots, sometimes for emotional support. Each answer trades protection against privacy, access and possible benefit, and decides who must prove what before a young person can use the product.
Access with design duties
Keep access but require design safeguards: disclosure that the user is talking to an AI, limits on simulated relationships, age-appropriate defaults and crisis protocols.
No mandatory identity checks
Requiring age verification exposes everyone’s identity data to breaches and ends anonymity, so it should not be the price of protecting children.
Restrict by age
Set minimum ages and require services to check them, especially where companions can produce harmful material.
Keep minors off companion platforms
The risks to young people outweigh the benefits, so no one under 18 should use AI companion platforms.
Question under dispute
Augment or automate judgment?
When should AI assist a human decision, and when should the decision itself be handed over?
Why it matters. The answer decides who keeps authority and accountability, whether a person in the loop is real protection or reassurance, and who bears the errors of people and machines.
Keep a human in control
For high-risk uses, systems should be designed so that people can oversee them, stay alert to over-reliance, and override or reverse their outputs. (Article 14 cited at source level; passage check pending.)
Oversight must be shown to work
Requiring a human in the loop often fails because people cannot perform the oversight asked of them; agencies should instead justify adopting an algorithm and show evidence that oversight works.
Some decisions are better made by algorithms
In some settings, algorithms can outperform human judgment on the outcomes that matter, and a human override can reintroduce noise and bias.
Question under dispute
Local-language AI: build our own or adopt global models?
Should communities and regions build their own language technology, or adopt and adapt models built by global developers?
Why it matters. The choice decides whose languages work well, who controls the data and the models, and whether communities shape the tools or only receive them.
Build with communities
Language technology should be built through participatory research with speakers and local institutions, because the gap is social as well as technical and global models still underperform on many local languages.
Adopt and adapt global models
Large multilingual models trained across hundreds of languages and released openly can reach many languages faster than separate efforts.
Pool regional capacity
Regions can build shared models together through public institutions, so that their languages and culture are represented without each country starting alone.