If you don't care about your privacy, that's your privilege.
But as a society, we can't afford to give up on privacy,
just like we can't afford to give up on free speech. My name
is Arvind Narayanan. I'm a professor of computer science at
Princeton University. One of the areas that I study is AI. I've
also done a lot of research on privacy. What connects these
topics is, I'm motivated by tech accountability. Back in 2006,
Netflix released the movie viewing records of a half a
million of their customers. And this was for the purpose of a
machine learning competition. What they had said was that
because the customers had been de-identified because their
names were not attached to those movie viewing records, there was
nothing to be concerned about from a privacy perspective. At
the time, I was doing my PhD. And so my advisor, Vitaly
Shmatikov and I, we smelled a rat. I wondered, could we cross
correlate people's reviews on IMDB with the set of movies that
they had watched on Netflix. And we were able to show that that
was possible in some cases. So that's when I first started
recognizing that the assurances that companies might give people
on privacy might not, in fact, stand up to scrutiny. One of the
other areas where I was hearing this a lot is, yeah, yeah, we're
tracking everything you're doing on your apps, but it's all
anonymous. So we started to look into that. And we just, you
know, we fell into this whole minefield of, you know, the
really, really invasive, sneaky tracking that's happening on our
devices. And no, it's not really all anonymous, because it might
be attached to something like your so called hashed email
address, and it can be used to connect all of your profiles
from one device to another. And so it can still be used to build
this one kind of uber profile of you. And there are dozens of
companies that specialize in doing that. One thing we didn't
expect, when we started doing all this privacy research is
that it would be Silicon Valley versus Silicon Valley. Today,
probably the biggest protector of privacy is Apple, perhaps
even more effective than a lot of the privacy regulations
around the world, which are certainly also important. A lot
of AI technologies today do have powerful capabilities, but they
also have important limits. And so we should be very careful
about relying on them. There are many kinds of technologies that
are clubbed under this umbrella term AI. Often, these
technologically have very little to do with each other, except
that they're all forms of machine learning, they learn
from data. And just because technology is quickly moving in
one type of AI, most notably generative AI recently, doesn't
mean that all of these other applications of AI are getting
better or more accurate. It doesn't mean necessarily that
they should be used at all. I've had this suspicion for many,
many years now that a lot of the AI technologies that are being
sold to predict people's future behavior don't actually work.
These kinds of algorithms are used by banks for lending.
They're used by insurance companies. This is happening in
hiring when people apply for jobs. This is happening even in
the criminal justice system, when it comes to decisions about
bail and parole. When you look at the accuracy of these models,
it's barely better than random. So is it ethical to make these
consequential decisions about people when they're only
slightly more accurate than the flip of a coin? That's one issue
with these things. There are Well known racial biases and
other kinds of biases in these models. As a kid, I thought of
technology as automatically a force for good. But what I
started to realize growing up that what tech does is it
amplifies both the best and the worst of society. Machine
learning algorithms are fundamentally about finding
patterns in a data set, and then reflecting those patterns in
their outputs. So ultimately, any of these datasets is
generated by people. And people live in a society. And so there
are both individual biases and the structural biases of society
itself, that are almost always captured. When it comes to AI
and fairness, who gets to decide is really the elephant in the
room. And I think that's perhaps the most important question.
Does the decision maker or the developer of the algorithm, have
a process to get community input from various stakeholders from
the people who are going to be subjected to these decisions and
try to figure out what all the different trade offs are? I
think even when a technology gets very rapidly deployed, and
it's beneficial to those in positions of power, when
everyday people feel that it's unjust, they can and have been
able to successfully push back and that should be front of mind
for us when we see dubious applicantions of technology, we
shouldn't take it for granted