[MUSIC]
One interesting aspect of what's happening now is they increase
the use of artificial intelligence both in the scientific study but
also in platforms like Facebook, Google, Google Maps, and so on.
So, people often do not realize that the news feed that
they are seeing is basically an AI, artificial intelligence,
that is just working hard to keep them engaged as much as possible, right?
So in a way by interacting with Facebook,
they're interacting with artificial intelligence.
Which is an interesting thought, because very often people would ask me hey,
so when am I going to start interacting with robots, right?
I'm like, you're interacting with robots for [LAUGH] quite some time already,
you just don't realize that, right?
What you see on Facebook is basically a product of an artificial intelligence
trying to make you happy and make you engaged.
And now this has far-reaching consequences.
Let me give you an example.
Many people say hey, tools are never bad or good.
They're morally neutral.
Take a knife.
Knife is not bad or good.
It's just humans that can use knife for bad or good purposes.
Now interestingly, people were saying the same thing about artificial intelligence.
They would say hey, artificial intelligence cannot be evil.
It's just that the human user of artificial intelligence can be evil and
can leash out artificial intelligence to do evil things.
But it is not true.
Artificial intelligence can in fact
be evil regardless of the intentions of it's administrators.
Let me give you an example.
When you set up an artificial intelligence system, you give it a goal, right?
So if I'm Facebook, I will set my artificial intelligence
system to keep users engaged and,
well, maximize more my advertising revenues as well.
So, I set up this very complicated AI system that basically
optimizes your experience as a user, suggests friends to you, suggests
contents to you on your news feed, shows you the relevant adverts and so on.
And the goal of this AI system is to keep you engaged.
Now the very reason that we use artificial intelligence is that because
the tasks that our artificial intelligence is dealing with are too complicated for
humans to deal with on our own.
Right, so you could imagine hiring millions of editors that would sit down
and try to optimize, you know, your Facebook experience for you.
Right, pick, you know, just the stories for
you but obviously this is just not doable.
So what we're having here,
we replaced potential human editors with AI that has a goal of keeping you engaged.
Now the problem is that the goal might be
morally neutral or positive, like keeping you engaged with Facebook,
but the ways of achieving this goal, AI develops on its own.
So no one really tells exactly AI how to keep you engaged.
And AI will come up with ways of its own.
Now, the problem is that those ways might be ethically questionable.
Fake news, for instance.
We had this epidemics of fake news on Facebook.
Why?
Because AI recognized that if it's shows you this content,
then you will basically be engaged.
And you'd be like, my God, the story is crazy and people would comment and
some people would say, that's true, some other people say, that's rubbish.
So basically what happened that AI boosted user engagement.
Realize, in an excellent way, the goals that its designers asked it to achieve.
But achieved it in ways that humans would probably see as potentially unethical.
[MUSIC]
So the ideas behind the algorithm, like when you hear machine learning, or
neural networks, or deep learning networks.
Ideas behind those models, those artificial intelligence models,
are really simple.
You could explain them to a seven year old really easily.
But what happens is that those very simple rules that are used to create those
models, which are basically just, very often this is just multiplication and
addition that are being used to build those predictive models.
But then when you add up millions of elements in your equation or
even hundreds of elements, honestly, in your equation,
it's very easy for a computer to work with.
It's completely impossible for a human being to comprehend.
Which creates an interesting situation in which you have individuals and
societies making decisions based on what AI told them to do without
having an understanding why this decision has been made.
Google Maps, you drive to work in the morning,
Google Maps tells you how to go there.
You kind of don't really question the machine anymore.
You know that it's way better than your friends, neighbors and
partners in telling you how to beat the traffic.
Now, how does algorithms work exactly?
We know what are the rules driving this algorithm, but
how exactly the decision for you has been made?
This might be already beyond human comprehension.
How the stories were chosen for you for your Facebook news feed.
We know the simple rules that we ask the machine to follow.
But exact intricacies of this machine learning model that is actually driving
the content, driving the choices of the content to appear in news feed,
beyond our comprehension.
We have now computers analyzing x-rays in order to detect cancer and
suggest the best treatment and we know
that those computers are more accurate than doctors in making those decisions.
And we have very limited understanding
why exactly this particular decision has been made.
Well, you can ask the model, the model will be like, hey, this is really simple.
Here is one million numbers that drove me to make this decision.
Can a human being comprehend, you know, 1 million or even, you know,
100 numbers simultaneously?
Our brains did not evolve to work in this way.
[MUSIC]
Courts makes the decisions.
Make the decisions about whether to let people go and how long the sentence should
be and whether to grant you a parole based on the outcome of an algorithm.
So obviously it's a judge that slams the hammer down the, whatever,
podium but at the end of the day there is a little screen here and the screen says.
The algorithm calculated that this person should get seven years in prison, or
you should not grant parole to this person because their chances of re-offending
is 70 or 80%.
And this number is a product of hundreds of variables that go into it.
So no human being can understand, there is a mathematical equation, so
it's a deterministic process.
We know that it works because you can apply it to previous cases.
In fact, it works for the benefit of humans.
Because what happens is that when judges sees you for
few minutes and, you know, has few seconds to kind of skim through your case, and
you are unlikely to be black.
Then you basically just go to prison.
Because this is your safest option.
Just another 100 people in prison who cares, right?
This is the safest option.
But now, what happens is there is this algorithm there that says,
in fact, your intuition is to put them in prison but
they're unlikely to re-offend so why don't you let them go.
So we have evidence that those algorithms, in fact, are making prisons more
empty because they let judges be more liberal with their choices.
But on the other hand, we have the length of your sentence being decided not by
human being any more, but by a computer.
The time of an algorithm is way cheaper than the time of a judge.
So what will happen now, it will actually prosecution or they will, so
counties buy this software.
There are few companies in the US that produce this software and
it's basically a big data software.
They analyze life histories and cases,
case materials, and some demographic variables like number of siblings,
number of children, how many convictions you've had in the past, and what not.
And then they check how likely this person was to re-offend.
And you build this huge statistical model and it basically says,
okay a person that has three siblings and did not have this offense in the past,
or would not, is unlikely to re-offend so why don't you let them go.
And the prosecutor will actually give it to the judge.
It's kind of printed out of his certified,
accepted in a given jurisdiction, computer program.
And the judge will be like, okay.
Now there is an interesting psychological phenomena here because
if the judge disagrees with the program,
there's only one way in which a judge can disagree with a program.
Namely, the judge could decide to give you a longer sentence.
Because now imagine no human being would risk saying,
software says this person is likely to re-offend.
I will let them go.
Why? Because then the judge takes
a responsibility for
shortening the sentence against the advice of the computer overloads.
Which [LAUGH] basically means that you don't have judges disagreeing with
the algorithm because why would they take this responsibility?
If the person re-offends,
they can always say look, according to the model, it was unlikely to happen.
I just followed the advice of the model.
But if the model says this person is going to re-offend and judge lets you go and
then someone comes after you and says, hey why did you let this guy go?
You, what did you say, you acted against the, what the algorithm said.
So literally, we have cases of freedom and
imprisonment decided now by a mathematical formula that no one understands.
Because people don't understand how those formulas work.
It's highly disappointing, but
the fact is that humans are just no better judges of each other than algorithms are.
And if anything, algorithms are better judges of us than humans are.
Because algorithms are not sexist, are not racist, are not ageist and
are basically not biased against you.
We have so much evidence, if you have a black defendant and a white defendant,
exactly the same history,
black just basically will always get a larger sentence.
Now if it were a computer,
it will not happen because computer doesn't care about the color of your skin.
So you could argue that basically employing algorithms in such cases, and
we have a lot of evidence it actually lowers the sentence length and
lowers the re-offending rates.
So it's great for the society and great for the defendants.
The problem that I have with it, and I don't know how to solve it, is that we
follow those decisions and we have no idea how those decisions have been made.
[MUSIC]