72. Margot Gerritsen - Does AI have to be understandable to be ethical?
Towards Data Science
02/24/21
•82m
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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.
Previous Episode
71. Ben Garfinkel - Superhuman AI and the future of democracy and government
February 17, 2021
•68m
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.
Next Episode
73. David Roodman - Economic history and the road to the singularity
March 3, 2021
•68m
There’s a minor mystery in economics that may suggest that things are about to get really, really weird for humanity.
And that mystery is this: many economic models predict that, at some point, human economic output will become infinite.
Now, infinities really don’t tend to happen in the real world. But when they’re predicted by otherwise sound theories, they tend to indicate a point at which the assumptions of these theories break down in some fundamental way. Often, that’s because of things like phase transitions: when gases condense or liquids evaporate, some of their thermodynamic parameters go to infinity — not because anything “infinite” is really happening, but because the equations that define a gas cease to apply when those gases become liquids and vice-versa.
So how should we think of economic models that tell us that human economic output will one day reach infinity? Is it reasonable to interpret them as predicting a phase transition in the human economy — and if so, what might that transition look like? These are hard questions to answer, but they’re questions that my guest David Roodman, a Senior Advisor at Open Philanthropy, has thought about a lot.
David has centered his investigations on what he considers to be a plausible culprit for a potential economic phase transition: the rise of transformative AI technology. His work explores a powerful way to think about how, and even when, transformative AI may change how the economy works in a fundamental way.
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