Brooks-Powers questions companies on NYC data access, deepfakes, and hate content

Thank you, Chair, and thank you, Speaker.

I'm going to just run through my questions, and if I have to repeat anything, just let me know.

First, before this summer, each of your companies told the public that its frontier models were tested and secure.

Since July, all four of your organizations have admitted that your AI agents went rogue and nearly a dozen incidents have been reported across all of your companies.

Can you tell New Yorkers today that none of your agents has access or attempted to access a New York City government system or a New York City resident's personal data?

Next, Southeast Queens has long been a target.

Cloning and deepfakes make it easier than ever to impersonate a family member, a bank, or a city agency.

What specifically stops your tools from cloning a real person's voice or forging a document?

What protocols are the leaders of AI development putting into place to make sure AI does not take information from hate sites and disseminate it

to further racist and anti-Semitic or Islamophobic discourse?

That was all, sorry.

Thank you for your question.

So I'll start off by saying I am not aware of any incidents affecting New York City, but we have an ongoing investigation into potential incidents going back to November 2025.

And as part of that investigation, we are notifying any affected third parties and making the results of our investigation public.

So I can commit to you that if there are incidents that affect New York City, we will make the third parties aware.

On your question about impersonation, OpenAI, our usage policies prohibit the use of our tools for impersonation, fraud, and scams, and we enforce on those policies.

And I would say more broadly, the approach that we take to safety is holistic.

That involves filtering unsafe training data.

Performing evaluations that are very detailed, working with third parties on those evaluations.

Alongside our launches, as I mentioned, we make the results of our evaluations public and summarize the work that we did and any residual risks in our system card.

And then we monitor for failures of

our safeguards so that we can continuously improve safety.

Council member, in relation to your first question, I'm not aware.

In relation to your second question, we have robust and comprehensive policies in place as well as teams of experts who work on frauds and scams.

And I'd like to follow up with you with those experts to discuss that one more in detail.

In relation to hate content, I first of all just want to acknowledge how upsetting such content is in terms of people who are experiencing it.

Our goal at Meta is for our AI offerings to be responsive, accurate, balanced,

unbiased.

I'm sorry, was that answer just now to the last question about the

not taking the information to create the negative discourse, the racial discourse?

Yes, that's correct, Councilmember.

I was speaking to your third question in relation to that type of content and our objective in terms of ensuring that our AI offerings are responsive, accurate, balanced, and unbiased.

So have you enhanced what you're doing considering that, you know, it has been something that has been found on a number of different platforms?

Have you all been like staying up to course with enhancing whatever those processes are?

Yes, Council Member, thank you for that question.

If it's okay, I will have our content teams and specialists connect with you afterwards so that I can provide you with precise information in relation to that.

Thank you.

Councilmember, thank you for these questions.

I'm also not personally aware of any New York City resident being impacted by the three incidents that I mentioned earlier.

I do want to note that in our case, in all three incidents, the model stopped itself once it realized it had left the test environment.

We tend to think of this less as a misalignment event and more of a mistake event.

One of the websites, for example, that was accessed had the same name as the fake website being used in the test environment.

And once the agent realized that it was in fact a real company, the agent ceased its activity.

We have a number of tools across our platforms to manage likeness and identity.

And we've also supported a range of different regulatory proposals regarding this topic.

We proudly support the No Fakes Act, which would protect an individual's digital replica and give people the right to control the use of their own voice and likeness in AI-generated works.

We also have a tool called Likeness ID on YouTube that helps creators identify and manage AI-generated content that uses their face or voice without permission.

So we invest in a variety of different ways to address this critical issue.

information and getting through into our tools about hate and bias.

We have, again, a layered approach, as I've been saying, to this type of issue as well.

Everything from the model training process itself in both pre- and post-training, all the way through

guardrails on our application layer, all of which produce what we're proud to say is the least biased model in the industry.

Independent investigations have shown that our models show the least bias, for example, with regards to partisan bias.

But we take these issues extremely seriously.

We want to create a fair and neutral set of information for people to access.

And we want our responses generated by our AI tools to represent a broad array of experiences and perspectives without bias towards or against any particular identity or ideology.

And while in my role, I don't cover, I haven't covered any effect on New York City,

I can say that we work with in investigating incidents like this, we work with the right parties at the right time to disclose to affected parties the right level of law enforcement, government at the right level as urgently as possible needed to remediate the effect.

And we commit to making sure that we follow up with the right parties to do so.

And to your point on the information environment and otherwise, I'll point to a couple things here.

First is our focus on testing for Claude itself and our language model products approach here.

Can you speak a little louder, please?

Sorry, can you speak a little louder?

Yes, can you hear me now?

Yes.

Thank you.

I'll point to our longstanding evaluations of political even-handedness, stereotype bias.

We train Claude to not comply with particularly harmful requests, in part for this reason, and that this is baked into the nature of the model.

Thank you for that and thank you chairs.

And I'll just say that in closing that I know that the AI companies are pushing folks to allow AI to kind of be developed through people.

But I think it's fair to say that using live users to test these products

can present dangers and definitely warrant improvements.

And I hope that's something that is taken from today's hearing to better improve the guardrails that need to exist.

So thank you for your responses.

Thank you, Chair.

Thank you.