#11 - At The Intersection of AI, Governments, and Google - Tim Hwang

**Craig Cannon** (0:00)
Hey, this is Craig Cannon, and you're listening to Y Combinator's podcast. So today's episode is with Tim Hwang. Tim's the global public policy lead on AI and machine learning for Google. And what that basically means is he interacts with governments to inform Google's opinions on policy.
He also helps educate governments on what things like machine learning actually mean, and helps them figure out what the implications might be. So in this episode, Tim walks us through how governments are thinking about AI, and he also shares some thoughts on what the future might look like.

**Tim Hwang** (0:30)
All right, here we go.

**Craig Cannon** (0:31)
And so I think that with AI and then policy on AI, you've kind of nested two obscure things that people don't really know what you're talking about. So could you just back up a little bit and explain what doing policy for Google actually means in the context of AI?

**Tim Hwang** (0:48)
Sure, definitely. So I think the really interesting thing about AI is basically that a lot of the modern techniques in artificial intelligence, if you've even asked people a decade ago, they would have told you, like, this is never going to be a thing. It's a complete dead end. Why are you doing this research?
And it really has kind of exploded in a completely unexpected way in the last few years. And so really a lot of the challenge has been, like, OK, everybody's kind of wrapping their heads around what the, even what the business impact of the technology is going to be. But there's increasingly a lot of people trying to figure out what the social impact of the technology will be. And I would say policy really sits at that interface between these really cool technological capabilities that are coming about and then what society in general is going to do about it.

**Craig Cannon** (1:27)
And so what would be a tangible example at Google of a policy that you guys have worked on to figure out?

**Tim Hwang** (1:34)
Sure, so there's a couple of really interesting problems that we've been working on very closely. So one of them is this question about fairness in machine learning systems.
And for example, to give you one really concrete challenge we've been thinking a lot about is in order to de-bias a system, once a machine learning system is behaving in a biased way, one way of trying to deal with it is collecting more diverse data.
But one of the big problems is when you do that, you end up collecting lots and lots of data about minorities, which raises all these really interesting questions around privacy and then what have you. And that ends up being a really interesting problem because it's both a technical challenge, which is, can you collect an adequately diverse data set? But on the other hand, also this policy question, which is, what is society comfortable with you collecting? And what are the practices? And that ends up being a really interesting trade-off that you have to navigate if you're interested in these problems.

**Craig Cannon** (2:21)
And so what do you actually have to do? Are you going doing user interviews with people, or is it just guessing?

**Tim Hwang** (2:28)
Yeah, part of it's user interviews. Part of it's actually working with people who know. It turns out that issues of privacy, particularly minority privacy, are not new problems.
And so a lot of our work is actually talking with people who are experts in that space. People have worked on bias and discrimination questions on the past, and a lot of data scientists are trying to get them to talk to one another. Because I think right now, what we're really trying to do is bridge these human values, on one hand, with a lot of what's happening on the technological side.

**Craig Cannon** (2:55)
And so if I'm a company, and I'm like, I can't afford a policy guy like Tim, and I will be dealing with large amounts of data that may or may not discriminate against people, are there any obvious no-goes that you would tell someone?

**Tim Hwang** (3:09)
Well, I think it's to be sure that you're interrogating the data, right? I think that's one important place to start. Now, I think one of the interesting things about machine learning is that there's lots of potential points of failure. And I think every single interesting point of failure is being investigated right now.
But I mean, one of the most common problems is just that you don't adequately think through your data. And so the machine does what the machine does, right, which is trying to optimize against your objective function that you give it. And it will often maximize in ways that you don't expect. And that is, in fact, part of the problem, right? So, I mean, one of the examples that I always think about is, you know, we have this project that we released, it was called Deep Dream. And one of the problems in computer vision is trying to figure out, like, what the computer actually thinks it sees when it looks at an image.