Light up the world
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.”
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.
广告商 甚至政府部门 都对此十分感兴趣
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.
所以我就告诉自己 嘿 去技术领域混吧
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.
好吧 哈 哈 哈 都在笑我
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 reoffend?”
“Which news item or movie should be recommended to people?”
看吧 没错 我们确实已经用了一段时间的计算机
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
and instructed to find and hire
people like the existing high performers in the company.
I once attended a conference
that brought together human resources managers and executives,
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 mean, 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,
她会说 “泽伊内普 咱们去吃午饭吧”
and she’d say, “Zeynep, let’s go to lunch!”
I’d be puzzled by the weird timing.
It’s 4pm. 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 postpartum depression
from social media data.
The results are impressive.
Her system can predict the likelihood of depression
months before the onset of any symptoms —
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 managers 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?
如果雇佣了充满侵略性的人 仅仅是因为这符合你们的办公室文化 又会怎样呢”
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?”
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.
很显然她想说 这又不是我的错 走开 然后对我使用“死亡凝视”
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,
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 picking up her godsister
from a school in Broward County, Florida,
running down the street with a friend of hers.
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.
她确实错了 确实犯傻了 但她只有十八岁
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.
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 what to show you
from all the friends and pages 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:
像点赞 分享 和评论
likes, shares, comments.
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 was 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.
理由很充分 要么倒冰水 要么捐款做慈善 不错
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:
“Its largest airport is named for a World War II hero,
its second-largest for a World War II battle.”
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.
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.
我都不用去想 就能知道 如果换成
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,
不论是在法院 新闻业 还是在战场…
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.
Artificial intelligence does not give 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.