69. Anders Sandberg - Answering the Fermi Question: Is AI our Great Filter?
Towards Data Science
02/03/21
•65m
About
Comments
Featured In
The apparent absence of alien life in our universe has been a source of speculation and controversy in scientific circles for decades. If we assume that there’s even a tiny chance that intelligent life might evolve on a given planet, it seems almost impossible to imagine that the cosmos isn’t brimming with alien civilizations. So where are they?
That’s what Anders Sandberg calls the “Fermi Question”: given the unfathomable size of the universe, how come we have seen no signs of alien life? Anders is a researcher at the University of Oxford’s Future of Humanity Institute, where he tries to anticipate the ethical, philosophical and practical questions that human beings are going to have to face as we approach what could be a technologically unbounded future. That work focuses to a great extent on superintelligent AI and the existential risks it might create. As part of that work, he’s studied the Fermi Question in great detail, and what it implies for the scarcity of life and the value of the human species.
Previous Episode
68. Silvia Milano - Ethical problems with recommender systems
January 27, 2021
•60m
One of the consequences of living in a world where we have every kind of data we could possible want at our fingertips, is that we have far more data available to us than we could possibly review. Wondering which university program you should enter? You could visit any one of a hundred thousand websites that each offer helpful insights, or take a look at ten thousand different program options on hundreds of different universities’ websites. The only snag is that, by the time you finish that review, you probably could have graduated.
Recommender systems allow us to take controlled sips from the information fire hose that’s pointed our way every day of the week, by highlighting a small number of particularly relevant or valuable items from a vast catalog. And while they’re incredibly valuable pieces of technology, they also have some serious ethical failure modes — many of which arise because companies tend to build recommenders to reflect user feedback, without thinking of the broader implications these systems have for society and human civilization.
Those implications are significant, and growing fast. Recommender algorithms deployed by Twitter and Google regularly shape public opinion on the key moral issues of our time — sometimes intentionally, and sometimes even by accident. So rather than allowing society to be reshaped in the image of these powerful algorithms, perhaps it’s time we asked some big questions about the kind of world we want to live in, and worked backward to figure out what our answers would imply for the way we evaluate recommendation engines.
That’s exactly why I wanted to speak with Silvia Milano, my guest for this episode of the podcast. Silvia is an expert of the ethics of recommender systems, and a researcher at Oxford’s Future of Humanity Institute and at the Oxford Internet Institute, where she’s been involved in work aimed at better understanding the hidden impact of recommendation algorithms, and what can be done to mitigate their more negative effects. Our conversation took us led us to consider complex questions, including the definition of identity, the human right to self-determination, and the interaction of governments with technology companies.
Next 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.
If you like this episode you’ll love
Promoted