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Zeynep Tufekci: Machine intelligence makes human morals more important
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Machine intelligence is here, and we're already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that don't fit human error patterns -- and in ways we won't expect or be prepared for. "We cannot outsource our responsibilities to machines," she says. "We must hold on ever tighter to human values and human ethics."
Zeynep Tufekci - Techno-sociologist
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Techno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Double-click the English transcript below to play the video.
00:12
So, I started my first job
as a computer programmer
as a computer programmer
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in my very first year of college --
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basically, as a teenager.
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Soon after I started working,
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writing software in a company,
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a manager who worked at the company
came down to where I was,
came down to where I was,
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and he whispered to me,
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"Can he tell if I'm lying?"
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There was nobody else in the room.
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"Can who tell if you're lying?
And why are we whispering?"
And why are we whispering?"
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The manager pointed
at the computer in the room.
at the computer in the room.
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"Can he tell if I'm lying?"
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Well, that manager was having
an affair with the receptionist.
an affair with the receptionist.
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(Laughter)
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And I was still a teenager.
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So I whisper-shouted back to him,
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"Yes, the computer can tell
if you're lying."
if you're lying."
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01:03
(Laughter)
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Well, I laughed, but actually,
the laugh's on me.
the laugh's on me.
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Nowadays, there are computational systems
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that can suss out
emotional states and even lying
emotional states and even lying
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from processing human faces.
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Advertisers and even governments
are very interested.
are very interested.
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I had become a computer programmer
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because I was one of those kids
crazy about math and science.
crazy about math and science.
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But somewhere along the line
I'd learned about nuclear weapons,
I'd learned about nuclear weapons,
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and I'd gotten really concerned
with the ethics of science.
with the ethics of science.
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I was troubled.
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However, because of family circumstances,
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I also needed to start working
as soon as possible.
as soon as possible.
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So I thought to myself, hey,
let me pick a technical field
let me pick a technical field
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where I can get a job easily
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and where I don't have to deal
with any troublesome questions of ethics.
with any troublesome questions of ethics.
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So I picked computers.
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(Laughter)
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Well, ha, ha, ha!
All the laughs are on me.
All the laughs are on me.
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Nowadays, computer scientists
are building platforms
are building platforms
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that control what a billion
people see every day.
people see every day.
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They're developing cars
that could decide who to run over.
that could decide who to run over.
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They're even building machines, weapons,
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that might kill human beings in war.
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It's ethics all the way down.
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02:19
Machine intelligence is here.
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We're now using computation
to make all sort of decisions,
to make all sort of decisions,
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but also new kinds of decisions.
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We're asking questions to computation
that have no single right answers,
that have no single right answers,
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that are subjective
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and open-ended and value-laden.
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We're asking questions like,
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"Who should the company hire?"
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"Which update from which friend
should you be shown?"
should you be shown?"
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"Which convict is more
likely to reoffend?"
likely to reoffend?"
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"Which news item or movie
should be recommended to people?"
should be recommended to people?"
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Look, yes, we've been using
computers for a while,
computers for a while,
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but this is different.
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This is a historical twist,
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because we cannot anchor computation
for such subjective decisions
for such subjective decisions
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the way we can anchor computation
for flying airplanes, building bridges,
for flying airplanes, building bridges,
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going to the moon.
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Are airplanes safer?
Did the bridge sway and fall?
Did the bridge sway and fall?
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There, we have agreed-upon,
fairly clear benchmarks,
fairly clear benchmarks,
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and we have laws of nature to guide us.
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We have no such anchors and benchmarks
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for decisions in messy human affairs.
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To make things more complicated,
our software is getting more powerful,
our software is getting more powerful,
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but it's also getting less
transparent and more complex.
transparent and more complex.
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Recently, in the past decade,
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complex algorithms
have made great strides.
have made great strides.
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They can recognize human faces.
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They can decipher handwriting.
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They can detect credit card fraud
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and block spam
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and they can translate between languages.
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They can detect tumors in medical imaging.
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They can beat humans in chess and Go.
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Much of this progress comes
from a method called "machine learning."
from a method called "machine learning."
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Machine learning is different
than traditional programming,
than traditional programming,
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where you give the computer
detailed, exact, painstaking instructions.
detailed, exact, painstaking instructions.
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It's more like you take the system
and you feed it lots of data,
and you feed it lots of data,
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including unstructured data,
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like the kind we generate
in our digital lives.
in our digital lives.
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And the system learns
by churning through this data.
by churning through this data.
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And also, crucially,
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these systems don't operate
under a single-answer logic.
under a single-answer logic.
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They don't produce a simple answer;
it's more probabilistic:
it's more probabilistic:
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"This one is probably more like
what you're looking for."
what you're looking for."
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Now, the upside is:
this method is really powerful.
this method is really powerful.
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The head of Google's AI systems called it,
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"the unreasonable effectiveness of data."
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The downside is,
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we don't really understand
what the system learned.
what the system learned.
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In fact, that's its power.
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This is less like giving
instructions to a computer;
instructions to a computer;
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it's more like training
a puppy-machine-creature
a puppy-machine-creature
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we don't really understand or control.
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So this is our problem.
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It's a problem when this artificial
intelligence system gets things wrong.
intelligence system gets things wrong.
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It's also a problem
when it gets things right,
when it gets things right,
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because we don't even know which is which
when it's a subjective problem.
when it's a subjective problem.
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We don't know what this thing is thinking.
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So, consider a hiring algorithm --
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a system used to hire people,
using machine-learning systems.
using machine-learning systems.
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Such a system would have been trained
on previous employees' data
on previous employees' data
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and instructed to find and hire
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people like the existing
high performers in the company.
high performers in the company.
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Sounds good.
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I once attended a conference
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that brought together
human resources managers and executives,
human resources managers and executives,
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high-level people,
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using such systems in hiring.
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They were super excited.
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They thought that this would make hiring
more objective, less biased,
more objective, less biased,
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and give women
and minorities a better shot
and minorities a better shot
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against biased human managers.
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And look -- human hiring is biased.
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I know.
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I mean, in one of my early jobs
as a programmer,
as a programmer,
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my immediate manager would sometimes
come down to where I was
come down to where I was
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really early in the morning
or really late in the afternoon,
or really late in the afternoon,
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and she'd say, "Zeynep,
let's go to lunch!"
let's go to lunch!"
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I'd be puzzled by the weird timing.
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It's 4pm. Lunch?
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I was broke, so free lunch. I always went.
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I later realized what was happening.
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My immediate managers
had not confessed to their higher-ups
had not confessed to their higher-ups
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that the programmer they hired
for a serious job was a teen girl
for a serious job was a teen girl
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who wore jeans and sneakers to work.
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I was doing a good job,
I just looked wrong
I just looked wrong
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and was the wrong age and gender.
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So hiring in a gender- and race-blind way
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certainly sounds good to me.
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But with these systems,
it is more complicated, and here's why:
it is more complicated, and here's why:
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Currently, computational systems
can infer all sorts of things about you
can infer all sorts of things about you
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from your digital crumbs,
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even if you have not
disclosed those things.
disclosed those things.
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They can infer your sexual orientation,
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your personality traits,
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your political leanings.
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They have predictive power
with high levels of accuracy.
with high levels of accuracy.
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Remember -- for things
you haven't even disclosed.
you haven't even disclosed.
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This is inference.
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I have a friend who developed
such computational systems
such computational systems
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to predict the likelihood
of clinical or postpartum depression
of clinical or postpartum depression
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from social media data.
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The results are impressive.
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Her system can predict
the likelihood of depression
the likelihood of depression
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months before the onset of any symptoms --
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months before.
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No symptoms, there's prediction.
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She hopes it will be used
for early intervention. Great!
for early intervention. Great!
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But now put this in the context of hiring.
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So at this human resources
managers conference,
managers conference,
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I approached a high-level manager
in a very large company,
in a very large company,
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and I said to her, "Look,
what if, unbeknownst to you,
what if, unbeknownst to you,
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your system is weeding out people
with high future likelihood of depression?
with high future likelihood of depression?
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They're not depressed now,
just maybe in the future, more likely.
just maybe in the future, more likely.
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What if it's weeding out women
more likely to be pregnant
more likely to be pregnant
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in the next year or two
but aren't pregnant now?
but aren't pregnant now?
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What if it's hiring aggressive people
because that's your workplace culture?"
because that's your workplace culture?"
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You can't tell this by looking
at gender breakdowns.
at gender breakdowns.
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Those may be balanced.
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And since this is machine learning,
not traditional coding,
not traditional coding,
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there is no variable there
labeled "higher risk of depression,"
labeled "higher risk of depression,"
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"higher risk of pregnancy,"
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"aggressive guy scale."
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Not only do you not know
what your system is selecting on,
what your system is selecting on,
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you don't even know
where to begin to look.
where to begin to look.
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It's a black box.
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It has predictive power,
but you don't understand it.
but you don't understand it.
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"What safeguards," I asked, "do you have
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to make sure that your black box
isn't doing something shady?"
isn't doing something shady?"
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She looked at me as if I had
just stepped on 10 puppy tails.
just stepped on 10 puppy tails.
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(Laughter)
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She stared at me and she said,
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"I don't want to hear
another word about this."
another word about this."
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And she turned around and walked away.
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Mind you -- she wasn't rude.
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It was clearly: what I don't know
isn't my problem, go away, death stare.
isn't my problem, go away, death stare.
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(Laughter)
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Look, such a system
may even be less biased
may even be less biased
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than human managers in some ways.
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And it could make monetary sense.
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But it could also lead
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to a steady but stealthy
shutting out of the job market
shutting out of the job market
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of people with higher risk of depression.
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Is this the kind of society
we want to build,
we want to build,
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without even knowing we've done this,
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because we turned decision-making
to machines we don't totally understand?
to machines we don't totally understand?
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Another problem is this:
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these systems are often trained
on data generated by our actions,
on data generated by our actions,
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human imprints.
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Well, they could just be
reflecting our biases,
reflecting our biases,
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and these systems
could be picking up on our biases
could be picking up on our biases
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and amplifying them
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and showing them back to us,
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while we're telling ourselves,
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"We're just doing objective,
neutral computation."
neutral computation."
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Researchers found that on Google,
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women are less likely than men
to be shown job ads for high-paying jobs.
to be shown job ads for high-paying jobs.
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And searching for African-American names
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is more likely to bring up ads
suggesting criminal history,
suggesting criminal history,
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even when there is none.
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Such hidden biases
and black-box algorithms
and black-box algorithms
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that researchers uncover sometimes
but sometimes we don't know,
but sometimes we don't know,
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can have life-altering consequences.
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In Wisconsin, a defendant
was sentenced to six years in prison
was sentenced to six years in prison
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for evading the police.
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You may not know this,
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but algorithms are increasingly used
in parole and sentencing decisions.
in parole and sentencing decisions.
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He wanted to know:
How is this score calculated?
How is this score calculated?
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It's a commercial black box.
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The company refused to have its algorithm
be challenged in open court.
be challenged in open court.
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But ProPublica, an investigative
nonprofit, audited that very algorithm
nonprofit, audited that very algorithm
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with what public data they could find,
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and found that its outcomes were biased
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and its predictive power
was dismal, barely better than chance,
was dismal, barely better than chance,
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and it was wrongly labeling
black defendants as future criminals
black defendants as future criminals
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at twice the rate of white defendants.
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So, consider this case:
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This woman was late
picking up her godsister
picking up her godsister
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from a school in Broward County, Florida,
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running down the street
with a friend of hers.
with a friend of hers.
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They spotted an unlocked kid's bike
and a scooter on a porch
and a scooter on a porch
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and foolishly jumped on it.
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As they were speeding off,
a woman came out and said,
a woman came out and said,
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"Hey! That's my kid's bike!"
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They dropped it, they walked away,
but they were arrested.
but they were arrested.
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She was wrong, she was foolish,
but she was also just 18.
but she was also just 18.
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She had a couple of juvenile misdemeanors.
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Meanwhile, that man had been arrested
for shoplifting in Home Depot --
for shoplifting in Home Depot --
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85 dollars' worth of stuff,
a similar petty crime.
a similar petty crime.
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But he had two prior
armed robbery convictions.
armed robbery convictions.
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But the algorithm scored her
as high risk, and not him.
as high risk, and not him.
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Two years later, ProPublica found
that she had not reoffended.
that she had not reoffended.
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It was just hard to get a job
for her with her record.
for her with her record.
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He, on the other hand, did reoffend
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and is now serving an eight-year
prison term for a later crime.
prison term for a later crime.
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Clearly, we need to audit our black boxes
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and not have them have
this kind of unchecked power.
this kind of unchecked power.
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(Applause)
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Audits are great and important,
but they don't solve all our problems.
but they don't solve all our problems.
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Take Facebook's powerful
news feed algorithm --
news feed algorithm --
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you know, the one that ranks everything
and decides what to show you
and decides what to show you
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from all the friends and pages you follow.
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Should you be shown another baby picture?
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(Laughter)
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A sullen note from an acquaintance?
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An important but difficult news item?
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There's no right answer.
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Facebook optimizes
for engagement on the site:
for engagement on the site:
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likes, shares, comments.
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In August of 2014,
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protests broke out in Ferguson, Missouri,
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after the killing of an African-American
teenager by a white police officer,
teenager by a white police officer,
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under murky circumstances.
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The news of the protests was all over
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my algorithmically
unfiltered Twitter feed,
unfiltered Twitter feed,
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but nowhere on my Facebook.
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Was it my Facebook friends?
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I disabled Facebook's algorithm,
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which is hard because Facebook
keeps wanting to make you
keeps wanting to make you
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come under the algorithm's control,
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and saw that my friends
were talking about it.
were talking about it.
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It's just that the algorithm
wasn't showing it to me.
wasn't showing it to me.
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I researched this and found
this was a widespread problem.
this was a widespread problem.
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The story of Ferguson
wasn't algorithm-friendly.
wasn't algorithm-friendly.
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It's not "likable."
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Who's going to click on "like?"
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It's not even easy to comment on.
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Without likes and comments,
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the algorithm was likely showing it
to even fewer people,
to even fewer people,
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so we didn't get to see this.
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Instead, that week,
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Facebook's algorithm highlighted this,
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which is the ALS Ice Bucket Challenge.
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Worthy cause; dump ice water,
donate to charity, fine.
donate to charity, fine.
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But it was super algorithm-friendly.
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The machine made this decision for us.
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A very important
but difficult conversation
but difficult conversation
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might have been smothered,
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had Facebook been the only channel.
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Now, finally, these systems
can also be wrong
can also be wrong
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in ways that don't resemble human systems.
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Do you guys remember Watson,
IBM's machine-intelligence system
IBM's machine-intelligence system
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that wiped the floor
with human contestants on Jeopardy?
with human contestants on Jeopardy?
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It was a great player.
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But then, for Final Jeopardy,
Watson was asked this question:
Watson was asked this question:
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"Its largest airport is named
for a World War II hero,
for a World War II hero,
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its second-largest
for a World War II battle."
for a World War II battle."
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(Hums Final Jeopardy music)
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Chicago.
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The two humans got it right.
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Watson, on the other hand,
answered "Toronto" --
answered "Toronto" --
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for a US city category!
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The impressive system also made an error
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that a human would never make,
a second-grader wouldn't make.
a second-grader wouldn't make.
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Our machine intelligence can fail
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in ways that don't fit
error patterns of humans,
error patterns of humans,
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in ways we won't expect
and be prepared for.
and be prepared for.
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It'd be lousy not to get a job
one is qualified for,
one is qualified for,
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but it would triple suck
if it was because of stack overflow
if it was because of stack overflow
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in some subroutine.
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(Laughter)
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In May of 2010,
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a flash crash on Wall Street
fueled by a feedback loop
fueled by a feedback loop
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in Wall Street's "sell" algorithm
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wiped a trillion dollars
of value in 36 minutes.
of value in 36 minutes.
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I don't even want to think
what "error" means
what "error" means
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in the context of lethal
autonomous weapons.
autonomous weapons.
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So yes, humans have always made biases.
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Decision makers and gatekeepers,
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in courts, in news, in war ...
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they make mistakes;
but that's exactly my point.
but that's exactly my point.
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We cannot escape
these difficult questions.
these difficult questions.
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We cannot outsource
our responsibilities to machines.
our responsibilities to machines.
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(Applause)
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Artificial intelligence does not give us
a "Get out of ethics free" card.
a "Get out of ethics free" card.
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Data scientist Fred Benenson
calls this math-washing.
calls this math-washing.
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We need the opposite.
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We need to cultivate algorithm suspicion,
scrutiny and investigation.
scrutiny and investigation.
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We need to make sure we have
algorithmic accountability,
algorithmic accountability,
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auditing and meaningful transparency.
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We need to accept
that bringing math and computation
that bringing math and computation
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to messy, value-laden human affairs
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does not bring objectivity;
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rather, the complexity of human affairs
invades the algorithms.
invades the algorithms.
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Yes, we can and we should use computation
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to help us make better decisions.
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But we have to own up
to our moral responsibility to judgment,
to our moral responsibility to judgment,
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and use algorithms within that framework,
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not as a means to abdicate
and outsource our responsibilities
and outsource our responsibilities
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to one another as human to human.
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Machine intelligence is here.
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That means we must hold on ever tighter
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to human values and human ethics.
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Thank you.
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(Applause)
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ABOUT THE SPEAKER
Zeynep Tufekci - Techno-sociologistTechno-sociologist Zeynep Tufekci asks big questions about our societies and our lives, as both algorithms and digital connectivity spread.
Why you should listen
We've entered an era of digital connectivity and machine intelligence. Complex algorithms are increasingly used to make consequential decisions about us. Many of these decisions are subjective and have no right answer: who should be hired, fired or promoted; what news should be shown to whom; which of your friends do you see updates from; which convict should be paroled. With increasing use of machine learning in these systems, we often don't even understand how exactly they are making these decisions. Zeynep Tufekci studies what this historic transition means for culture, markets, politics and personal life.
Tufekci is a contributing opinion writer at the New York Times, an associate professor at the School of Information and Library Science at University of North Carolina, Chapel Hill, and a faculty associate at Harvard's Berkman Klein Center for Internet and Society.
Her book, Twitter and Tear Gas: The Power and Fragility of Networked Protest, was published in 2017 by Yale University Press. Her next book, from Penguin Random House, will be about algorithms that watch, judge and nudge us.
Zeynep Tufekci | Speaker | TED.com