Machine intelligence makes human morals more important

So I started my first job
as a computer programmer

in my very first year of college,

basically as a teenager.

Soon after I started working,

writing software in a company,

a manager who worked at the company
came down to where I was,

and he whispered to me,

"Can he tell if I'm lying?"

There was nobody else in the room.

"Can who tell if you're lying,
and why are we whispering?"

The manager pointed
at the computer in the room.

"Can he tell if I'm lying?"

Well, that manager was having
an affair with the receptionist,

and I was still a teenager,

so I whisper-shouted back to him,

"Yes, the computer can tell
if you're lying."

(Laughter)

Well, I laughed, but actually
the laugh's on me.

Nowadays, there are computational systems
that can suss out emotional states,

and even lying,

from processing human faces.

Advertisers and even governments
are very interested.

I had become a computer programmer
because I was one of those kids

crazy about math and science,

but somewhere along the line
I'd learned about nuclear weapons,

and I'd gotten really concerned
with the ethics of science.

I was troubled.

However, because of family circumstances,

I also needed to start working
as soon as possible.

So I thought to myself, hey,
let me pick a technical field

where I can get a job easily

and where I don't have to deal
with any troublesome questions of ethics.

So I picked computers.

Well ha ha ha, all the laughs are on me.

Nowadays, computer scientists
are building platforms that control

what a billion people see every day.

They're developing cars that
could decide who to run over.

They're even building machines, weapons,
that might kill human beings in war.

It's ethics all the way down.

Machine intelligence is here.

We're now using computation
to make all sort of decisions,

but also new kinds of decisions.

We're asking questions to computation
that have no single right answers,

that are subjective and open-ended
and value-laden.

We're asking questions like,

"Who should the company hire?"

"Which update from which friend
should you be shown?"

"Which convict is more
likely to re-offend?"

"Which news item or movie
should be recommended to people?"

Look, yes, we've been using
computers for a while,

but this is different.

This is a historical twist,

because we cannot anchor
computation for such subjective decisions

the way we can anchor computation

for flying airplanes, building bridges,
going to the moon.

Are airplanes safer?
Did the bridge sway and fall?

There we have agreed-upon,
fairly clear benchmarks,

and we have laws of nature to guide us.

We have no such anchors and benchmarks

for decisions in messy human affairs.

To make things more complicated,
our software is getting more powerful,

but it's also getting less transparent
and more complex.

Recently, in the past decade,

complex algorithms have made
great strides.

They can recognize human faces.

They can decipher handwriting.

They can detect credit card fraud
and block spam

and they can translate between languages.

They can detect tumors in medical imaging.

They can beat humans in chess and go.

Much of this progress comes from
a method called machine learning.

Machine learning is different
than traditional programming,

where you give the computer
detailed, exact, painstaking instructions.

It's more like you take the system
and you feed it lots of data,

including unstructured data,

like the kind we generate
in our digital lives.

And the system learns
by churning through this data.

And also crucially, these systems
don't operate under a single answer logic.

They don't produce a simple answer.

It's more probabilistic.

This one is probably more
like what you're looking for.

Now the upside is, this method
is really powerful.

The head of Google's AI systems called it
the unreasonable effectiveness of data.

The downside is we don't really
understand what the system learned.

In fact, that's its power.

This is less like giving instructions
to a computer.

It's more like training a puppy
machine creature

we don't really understand or control.

So this is our problem.

It's a problem when this artificial
intelligence system gets things wrong.

It's also a problem

when it gets things right,

because we don't even know which is which
when it's a subjective problem.

We don't know what this thing is thinking.

So consider a hiring algorithm,

a system used to hire people
using machine learning systems.

Such a system would have been trained
on previous employees' data

an instructed to find and hire
people like the existing

high performers in the company.

Sounds good.

I once attended a conference
that brought together

human resources, managers,
and executives, high-level people,

using such systems in hiring.

They were super-excited.

They thought that this would make hiring
more objective, less biased,

and give women
and minorities a better shot

against biased human managers.

And look, human hiring is biased.

I know.

In one of my early jobs as a programmer,

my immediate manager would sometimes
come down to where I was

really early in the morning

or really late in the afternoon,

and she'd say, "Zeynep,
let's go to lunch!"

I'd be puzzled by the weird timing.

It's 4 pm? Lunch?

I was broke, so free lunch. I always went.

I later realized what was happening.

My immediate managers
had not confessed to their higher-ups

that the programmer they hired
for a serious job was a teen girl

who wore jeans and sneakers to work.

I was doing a good job.

I just looked wrong and was
the wrong age and gender.

So hiring in a gender- and race-blind way

certainly sounds good to me.

But with these systems,
it is more complicated, and here's why.

Currently, computational systems
can infer all sorts of things about you

from your digital crumbs,

even if you have not
disclosed those things.

They can infer your sexual orientation,

your personality traits,

your political leanings.

They have predictive power
with high levels of accuracy,

remember for things
you haven't even disclosed.

This is inference.

I have a friend who developed
such computational systems

to predict the likelihood of clinical
or post-partum depression

from social media data.

The results were impressive.

Her system can predict
the likelihood of depression

months before the onset of any symptoms,

months before.

No symptoms, there's prediction.

She hopes it will be used
for early intervention.

Great.

But now put this in the context of hiring.

So at this human resources
manager's conference,

I approached a high-level manager

in a very large company,

and I said to her, "Look,
what if, unbeknownst to you,

your system is weeding out people with
high future likelihood of depression?

They're not depressed now,
just maybe in the future, more likely.

What if it's weeding out women
more likely to be pregnant

in the next year or two
but aren't pregnant now?

What if it's hiring aggressive people
because that's your workplace culture?"

You can't tell this by looking
at gender breakdowns.

Those may be balanced.

And since this is machine learning,
not traditional coding,

there is no variable there

labeled "higher risk of depression,"

"higher risk of pregnancy,"

"aggressive guy scale."

Not only do you not know
what your system is selecting on,

you don't even know
where to begin to look.

It's a black box.

It has predictive power,
but you don't understand it.

"What safeguards," I asked, "do you have
to make sure that your black box

isn't doing something shady?"

So she looked at me

as if I had just stepped
on 10 puppy tails.

She stared at me and she said,

"I don't want to hear
another word about this."

And she turned around and walked away.

Mind you, she wasn't rude.

It was clearly what I don't know
isn't my problem, go away, death stare.

Look, such a system may even be less
biased than human managers in some ways,

and it could make monetary sense,

but it could also lead to a steady but
stealthy shutting out of the job market

of people with higher risk of depression.

Is this the kind of society
we want to build

without even knowing we've done this

because we turned decision-making
to machines we don't totally understand?

Another problem is this:

these systems are often trained
on data generated by our actions,

human imprints.

Well, they could just be
reflecting our biases,

and these systems could be
picking up on our biases

and amplifying them

and showing them back to us,

while we're telling ourselves, "We're just
doing objective neutral computation."

Researchers found that on Google,

women are less likely than men

to be shown job ads for high-paying jobs,

and searching for African-American names

is more likely to bring up ads
suggesting criminal history,

even when there is none.

Such hidden biases
and black box algorithms

that researchers uncover sometimes

but sometimes we don't know

can have life-altering consequences.

In Wisconsin, a defendant was sentenced

to six years in prison
for evading the police.

You may not know this, but algorithms
are increasingly used

in parole and sentencing decisions.

He wanted to know,
how is this score calculated?

It's a commercial black box.

The company refused to have its algorithm
be challenged in open court,

but ProPublica, an investigative nonprofit

audited that very algorithm
with what public data they could find,

and found that its outcomes were biased

and its predictive power was dismal,
barely better than chance,

and it was wrongly labeling
black defendants

as future criminals

at twice the rate of white defendants.

So consider this case.

This woman was late to picking up
her godsister from a school

in Broward County, Florida,

running down a street
with a friend of hers,

and they spotted an unlocked
kid's bike and a scooter on a porch

and foolishly jumped on it.

As they were speeding off,
a woman came out and said,

"Hey, that's my kid's bike."

They dropped it, they walked away,

but they were arrested.

She was wrong, she was foolish,
but she was also just 18.

She had a couple of juvenile misdemeanors.

Meanwhile, that man had been arrested
for shoplifting in Home Depot,

85 dollars worth of stuff,
a similar petty crime,

but he had two prior
armed robbery convictions.

But the algorithm scored her
as high risk, and not him.

Two years later, ProPublica found
that she had not reoffended.

It was just hard to get a job
for her with her record.

He on the other hand did reoffend

and is now serving an eight-year
prison term for a later crime.

Clearly, we need to audit our black boxes

and not have them have
this kind of unchecked power.

(Applause)

Audits are great and important,
but they don't solve all our problems.

Take Facebook's powerful
news feed algorithm,

you know, the one that ranks everything

and decides to show you

what from all the friends
and pages that you follow.

Should you be shown
another baby picture?

A sullen note from an acquaintance?

An important but difficult
news item? There's no right answer.

Facebook optimizes
for engagement on the site:

likes, shares, comments.

So in August of 2014,

protests broke out in Ferguson, Missouri,

after the killing of an African-American
teenager by a white police officer

under murky circumstances.

The news of the protests
were all over my algorithmically

unfiltered Twitter feed

but nowhere on my Facebook.

Was it my Facebook friends?

I disabled Facebook's algorithm,

which is hard because Facebook
keeps wanting to make you

come under the algorithm's control,

and saw that my friends
were talking about it.

It's just that the algorithm
wasn't showing it to me.

I researched this and found
this was a widespread problem.

The story of Ferguson
wasn't algorithm-friendly.

It's not likable.

Who's going to click on like?

It's not even easy to comment on.

Without likes and comments,
the algorithm was likely showing it

to even fewer people,

so we didn't get to see this.

Instead, that week, Facebook's
algorithm highlighted this,

which is the ALS ice bucket challenge.

Worthy cause. Dump ice water,
donate to charity, fine.

But it was super-algorithm-friendly.

The machine made this decision for us.

A very important
but difficult conversation

might have been smothered

had Facebook been the only channel.

Now finally, these systems

can also be wrong in ways
that don't resemble human systems.

Do you guys remember Watson,
IBM's machine intelligence system

that wiped the floor with
human contestants on Jeopardy?

It was a great player.

But then, for Final Jeopardy,
Watson was asked this question:

"It's largest airport is named
for a World War II hero,

it's second largest
for a World War II battle."

Chicago.

The two humans got it right.

Watson, on the other hand,
answered "Toronto"

for a US city category.

The impressive system also made an error

that a human would never make,
a second grader wouldn't make.

Our machine intelligence can fail
in ways that don't fit

error patterns of humans,

in ways we won't expect
and be prepared for.

It'd be lousy not to get a job
one is qualified for,

but it would triple suck if it was
because of stack overflow

in some subroutine.

(Laughter)

In May of 2010, a flash crash
on Wall Street

fueled by a feedback loop
in Wall Street's sell algorithm

wiped a trillion dollars of value
in 36 minutes.

I don't even want to think
what error means in the context

of lethal autonomous weapons.

So yes, humans have always made biases.

Decision makers and gatekeepers,

in courts, in news, in war,

they make mistakes,
but that's exactly my point.

We cannot escape
these difficult questions.

We cannot outsource
our responsibilities to machines.

(Applause)

Artificial intelligence does not
get us a get out of ethics free card.

Data scientist Fred Benenson
calls this math-washing.

We need the opposite.

We need to cultivate algorithm suspicion,
scrutiny, and investigation.

We need to make sure we have
algorithmic accountability,

auditing, and meaningful transparency.

We need to accept
that bringing math and computation

to messy, value-laden human affairs

does not bring objectivity.

Rather, the complexity of human affairs
invades the algorithms.

Yes, we can and we should use computation
to help us make better decisions,

but we have to own up
to our moral responsibility to judgment

and use algorithms within that framework,

not as a means to abdicate
and outsource our responsibilities

to one another as human to human.

Machine intelligence is here.

That means we must hold on
ever tighter to human values

and human ethics.

Thank you.

(Applause)