Expert opinions on Regulations

Future risk reports, system cards and external evaluations should be assessed for reproducible methods, clear separation between simulated and real actions, disclosure of missed detections, and evidence that safety interventions do not simply make pr...

For the most advanced frontier AI models, an independent, federally overseen, industry-backed organization could set safety standards and verify independent, voluntary audits, similar to how other critical sectors are managed.

It is really important, given the stakes, that people who develop AI and those who test it don’t have relationships with each other.

You’ll see industry oppose outside auditors and the main reason is they would mandate something that can’t be done [today] in a robust and uniform manner.

The following practices are mapped to Australia’s 8 AI Ethics Principles, demonstrating how governments can practically apply them to their assurance of AI. [...] Governments should identify and consult with stakeholders, including subject matter and...

It is calling on the government to require AI companies to monitor and report severe loss of control incidents and to introduce emergency powers to manage severe loss of control incidents including temporarily restricting AI services.

Timelines for incident reports are often set with well-intentioned urgency, but if they implicitly assume that root-cause analysis or full impact assessment has already been conducted, they will either produce premature and unreliable reports or plac...

To maximize the effectiveness of such provisions, policymakers should define a sensible range of time frames to report incidents, possibly with a graduated approach according to the gravity of the harm.

For funders and international community: support and/or collaborate with local actors best placed to detect AI harm, and back efforts to establish cross-border AI incident information sharing.

Rather than creating entirely new channels, policymakers should focus on ensuring that existing channels can effectively incorporate information related to frontier models and agents. Building on trusted relationships will be faster and more effectiv...

We must come together to stop Flock and AI mass surveillance. That is why I will soon be introducing legislation to do just that.

This has effectively given police a very long leash to experiment with these technologies. And now we're in a tipping point situation where it's about to be expanded all over the country, and there's a real necessity for those legal frameworks to be ...

The new legal framework must include a meaningful regulatory regime which operationalises principles such as necessity, responsibility, proportionality, transparency, accountability and lawfulness. These principles should then structure and constrain...

What humanity needs is an international AI nonproliferation treaty.

The U.S. and China won’t move in lockstep on AI safety, and forging mutually binding agreements with real teeth is probably not possible right now.

We cannot allow America to become a surveillance state where a handful of AI oligarchs profit by giving the government the power to track our every movement.

The use of live facial recognition in our public spaces is an extraordinary expansion of state power that currently exists in a legal vacuum. We are not Luddites on these Benches; we recognise the utility of technology, but we must ensure that live f...

I have grave concerns that banning technology until it is legislated will choke innovation, it will drive genuine use cases away, and as technology is always developing, are we not in danger of ‘tomorrow never comes’ when it comes to legislation keep...

Those responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled.

Existing responsible AI governance frameworks mandate human oversight. Yet they share a critical assumption: that competent human overseers remain present and capable of exercising meaningful oversight and control over AI decisions. Accountability mu...

A lot of tools can tell you an AI agent is behaving strangely. Very few can act on that at the network layer, without waiting on a human to approve a ticket. That’s the piece we’re closing.

If an incident is detected or reported, you should always be able to ‘pull the plug’ and halt autonomous AI agent activity immediately.

If you’re relying on a ‘kill switch’ to stop an AI agent, you’ve already made a fundamental mistake. A properly governed agent is simply never handed the task, so there is nothing to kill.

About 1,400 citizens applied, and we randomly selected 200 of them to participate as members of the evaluation panel. [...] Ultimately, 185 citizens participated.

The Federal Communications Commission initiated this proceeding to protect consumer from unwanted AI-generated calls while ensuring that our rules do not hinder the potential benefits that AI can offer, including making telecommunications more readil...

If mandated AI safety reviews are to have any teeth, they must be accompanied by a ban on dangerous AI models that have failed their assessments. This means that some method must be found to restrict access to dangerous open weight AI models. [...] C...

A safety regime that restricts legitimate defenders, while capable models remain available for attackers, creates an asymmetric disadvantage.

We analyze approximately 497,000 candidate-vacancy pipeline entries from September 2017 to September 2022, covering seven pipeline stages that span automated processing, human discretion, candidate data, and employer decisions. Aggregate outcomes acr...

As with any technology, the need for regulation depends on how it is deployed, the nature of harm that may be caused, and whether existing measures are effective in addressing those harms. Where existing measures are assessed to be inadequate and a s...

The extensive disclosure, auditing, and human-review mandates could slow hiring processes, increase administrative costs, and discourage employers—especially smaller ones—from using tools that streamline workforce management.

Forcing companies to pay up when powerful AI systems cause harm may be one of the strongest incentives for preventing that harm in the first place.

It’s a very thorny area. It’s uncharted territory. There’s a good argument to be made on either side.

I just don’t think the developer is in a position to know exactly how their product is being used.