71. Ben Garfinkel - Superhuman AI and the future of democracy and government
Towards Data Science
02/17/21
•68m
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As we continue to develop more and more sophisticated AI systems, an increasing number of economists, technologists and futurists have been trying to predict what the likely end point of all this progress might be. Will human beings be irrelevant? Will we offload all of our decisions — from what we want to do with our spare time, to how we govern societies — to machines? And what is the emergence of highly capable and highly general AI systems mean for the future of democracy and governance?
These questions are impossible to answer completely and directly, but it may be possible to get some hints by taking a long-term view at the history of human technological development. That’s a strategy that my guest, Ben Garfinkel, is applying in his research on the future of AI. Ben is a physicist and mathematician who now does research on forecasting risks from emerging technologies at Oxford’s Future of Humanity Institute.
Apart from his research on forecasting the future impact of technologies like AI, Ben has also spent time exploring some classic arguments for AI risk, many of which he disagrees with. Since we’ve had a number of guests on the podcast who do take these risks seriously, I thought it would be worth speaking to Ben about his views as well, and I’m very glad I did.
Previous Episode
70. Sarah Williams - What does ethical AI even mean?
February 10, 2021
•45m
There’s no question that AI ethics has received a lot of well-deserved attention lately. But ask the average person what ethics AI means, and you’re as likely as not to get a blank stare. I think that’s largely because every data science or machine learning problem comes with a unique ethical context, so it can be hard to pin down ethics principles that generalize to a wide class of AI problems.
Fortunately, there are researchers who focus on just this issue— and my guest today, Sarah Williams, is one of them. Sarah is an associate professor of urban planning and the director of the Civic Data Design Lab at MIT’s School of Architecture and Planning School. Her job is to study applications of data science to urban planning, and to work with policymakers on applying AI in an ethical way. Through that process, she’s distilled several generalizable AI ethics principles that have practical and actionable implications.
This episode was a wide-ranging discussion about everything from the way our ideologies can colour our data analysis to the challenges governments face when trying to regulate AI.
Next Episode
72. Margot Gerritsen - Does AI have to be understandable to be ethical?
February 24, 2021
•82m
As AI systems have become more ubiquitous, people have begun to pay more attention to their ethical implications. Those implications are potentially enormous: Google’s search algorithm and Twitter’s recommendation system each have the ability to meaningfully sway public opinion on just about any issue. As a result, Google and Twitter’s choices have an outsized impact — not only on their immediate user base, but on society in general.
That kind of power comes with risk of intentional misuse (for example, Twitter might choose to boost tweets that express views aligned with their preferred policies). But while intentional misuse is an important issue, equally challenging is the problem of avoiding unintentionally bad outputs from AI systems.
Unintentionally bad AIs can lead to various biases that make algorithms perform better for some people than for others, or more generally to systems that are optimizing for things we actually don’t want in the long run. For example, platforms like Twitter and YouTube have played an important role in the increasing polarization of their US (and worldwide) user bases. They never intended to do this, of course, but their effect on social cohesion is arguably the result of internal cultures based on narrow metric optimization: when you optimize for short-term engagement, you often sacrifice long-term user well-being.
The unintended consequences of AI systems are hard to predict, almost by definition. But their potential impact makes them very much worth thinking and talking about — which is why I sat down with Stanford professor, co-director of the Women in Data Science (WiDS) initiative, and host of the WiDS podcast Margot Gerritsen for this episode of the podcast.
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