Expert opinions on Transparency

The Global Dialogue on Artificial Intelligence Governance is the first platform under a United Nations General Assembly mandate where Member States and all relevant stakeholders convene on AI governance.

We think that the smart way to balance these two truths is to allow AI developers to write their own safety standards, but require them to be transparent about what they are, so we can hold them to their own promises.

Our view is that AI is a general-purpose technology with a wide range of applications, and also that regulators understand well the sectors they are regulating. They understand the risks that are present and understand, as many noble Lords have raise...

SAFE confidentially collects and analyzes AI incidents and near misses, promptly informs affected parties and turns recurring failures into shared, evidence-based controls that reduce systemic risk.

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...

A more tractable near-term proposal is mandatory pre-release evaluation against a defined set of uplift benchmarks, particularly for chemical and biological capability, with results published before weights ship.

However, it is important to differentiate regulating applications (which we need) vs. regulating the technology (which is ill-advised).

Post-hoc explanations have no guarantee of being faithful, because the model was never trained to make them valid. The alternative is to make interpretability part of the training contract.

We have a very natural instinct to say: we want as much interpretability as possible. One of the sci-fi worry cases is that agents start speaking in languages to each other that we don't understand. [...] I bet there are ways to define boundaries tha...

[...] principles requiring that AI be explicable are misguided. We should be deciding which decisions require explanations. Automation is still an option;

By obtaining ISO 42001 certification, CallRail furthers its commitment to trust, providing independent validation that its AI systems are built, deployed, and monitored within a rigorous framework of ethical guardrails, continuous risk assessment, an...

This bill contains a critically flawed mandatory third-party audit requirement that creates an impossible compliance obligation on an unrealistic timeline.

It proposes a hybrid approach that combines enforceable AI-content labeling with platform-level interventions to inform voters in real time that messages were created or edited with AI-supported tools.

We should ask ourselves an important question: does the problem require government regulation?

Plan A is not that crazy, but it’s close: the algorithms are mandated to be open source, and while the weights aren’t (in fact the weights are banned from being OS), access to the weights is mandated to be open; members of the public can do evaluatio...

Open innovation is foundational to AI progress, and broad access to models, data, and tools is essential to accelerating breakthroughs. By adopting the Linux Foundation’s OpenMDW framework for NVIDIA open model families, we’re helping establish a sim...

A compute permit system would face its own challenges: defining which training runs qualify as high-stakes, preventing developers from fragmenting workloads to stay below thresholds, and ensuring that monitoring infrastructure does not become a tool ...

According to the proposed rule, a covered US person will be required to provide quarterly reports to BIS if the US person “engages in, or plans, within six months, to engage in ‘applicable activities.’” “Applicable activities” include: “Conducting a...

A regulatory trigger calibrated to compute volume does not measure what it purports to measure, and it was designed around a snapshot of model architecture that the industry has already moved beyond.