Nilay Patel

How do you make decisions?

What's your framework?

Let's put this into practice.

Let's talk about how AI might be changing your business and what you're doing.

The foundation here is that even the idea of the credit score is relatively recent.

This is a creation of basically the late 1980s.

And a lot of people can have a lot of feelings about their credit scores.

And I would say Experian, TransUnion, Equifax, you can have a lot of feelings about whether or not those companies are responsive to you if you have feelings about your credit score and where they come from.

In a world of AI, you have...

vastly more opportunity to make something richer in the data because you can query it differently.

You have vastly more opportunity to collect information because you can ingest more unstructured information and provide predictions.

And then you have vastly more risk

because the models might hallucinate the data, or they might reflect some underlying bias in the data set as a whole, or you might have huge security problems as we build out how the AI models might talk to each other in databases.

How do you evaluate all of that risk and still be trusted as Experian?

Because that seems like an awful amount of new risk as the technology shifts.

We need to take another quick break.

We're back with experienced Alex Lindner.

Before the break, Alex gave us a very long, detailed answer as to how he thinks about the inherent risk in AI systems.

But now I wanted to push on the gap between what we want these systems to do and what they can actually accomplish today.

You're describing a lot of math.