Liva Ralaivola: evaluation

Transcript context

…Yeah, it's a great question. And it's something that we in the industry need to do probably a better job of explaining and demystifying. For me, I think it all boils down to, you know, like transparency. So, you know, explaining to users what data is collected. So, for example, with Incrideo, we don't collect any personal information. So it's really, you know, a random anonymous ID. And then there's some things around, you know, what products people are interested in, you know, kind of what they've seen, what they like, what they don't like, and so on. And it's all about, for me, about a value exchange, right? So relevancy. So, you know, a system that knows nothing about you is going to show you random stuff that's irrelevant. And the brain has a really great way of filtering out irrelevant stuff. Whereas something that's truly interesting for you is way more engaging, it's way more resonant. And for a user, that creates a better experience. Also, I think in advertising, one of the great things is advertising is very much the lubricant that keeps the internet open and free, right? You know, that provides that allows service providers the ability to keep their services not behind paywalls. And so there's great utility in that. And advertising is what keeps that going. It's the revenue that those content and service providers get. that allows them to provide those great services to the end users. And, you know, providing transparency, the users can actually see what's happening and the ability to opt out. That's also something that's very important. You know, Criteo was a pioneer with the ad choices icon, so someone can click and they can see why they saw this ad and they give them the ability to opt out. Once you do that, then I think it provides great value to all the participants. Try to be like mentalists. We have very few information, very few cues, and we try to detect what is going to be the most relevant for each consumer and each end user so that in this value exchange, everyone's going to be happy down the road. So as a middle party, we have down the road the ones who have the least data and the most challenging tasks in terms of AI. And that's why actually on my part, I'm you know, because the challenge in terms of machine learning AI is really a big one and it's the most interesting one. Could we do kind of a double click on a couple aspects of that? One being like, if I were to open up my file, I'm not even sure if that's quite the right way to think about it. I'd be interested to know like what's in there. And I've occasionally clicked the sort of ad choices thing and seen, oh, you're seeing this ad because you're interested in skin care. And I'm like, OK, my wife got me this one. But I don't really know-- that's kind of a high-level sort of summary statement of why I'm seeing it. I don't know exactly what is under the hood. I'm also kind of confused about-- of course, we go to websites all the time, and we get this pop-up that says, accept cookies, don't accept cookies, what's going on with those cookies? I know there was a big change to the industry, and I think it was kind of driven by Apple a few years ago. Maybe it was driven by other parties as well, where the way in which information is gathered and the nature of that information was kind of changed. I think there were some winners and losers from that. I'm not quite sure how that really shook out or if we're back to essentially the status quo ante before those changes were made effectively. I'd love to hear just a little bit more concrete description of what the data is, and then that obviously feeds into what is the machine learning layer that sits on top of that data look like to make sense of it. Obviously, that data becomes the inputs, but I always like to get down to very brass tacks on what are the inputs and outputs of the models so we can really understand what it is that the AIs are doing for us.…

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