Cahill describes Meta's layered AI safety framework
Cahill explains Meta's safety approach under its superintelligence scaling framework, evaluating risk before release, working with external experts, and joining principles for robust internal controls and independent external evaluation.
Our commitment to safety is core to everything we do.
That means moving at the pace required to evaluate risks and validate safeguards, not treating safely as a one-time check.
We approach safety holistically through a layered approach.
For example, under our meta superintelligence scaling framework, before we release an AI model, we evaluate the model for risks and build safeguards to mitigate them.
We also work with external experts, industry partners, and government stakeholders as the technology and the science of evaluating AI continues to evolve.
We also recently joined other labs in signing onto a set of principles that set a floor for safe development, including robust internal controls,
independent external evaluation of whether those controls are operating as intended, and independent board committee oversight.
People will only embrace AI if they have confidence it works in alignment with people's intentions.
That confidence must be earned through rigorous safety work and transparency.
We're happy to serve as a resource for the city as you engage on these important issues.
Thank you.
Thank you so much.
And finally, we have Alice Friend at Google.