www.newyorker.com Open in urlscan Pro
151.101.0.239  Public Scan

Form analysis
1 forms found in the DOM

Name: newsletter — POST

<form class="form-with-validation NewsletterSubscribeFormValidation-iCYa-Dt dweEln" id="newsletter" name="newsletter" novalidate="" method="POST"><span class="TextFieldWrapper-Pzdqp hNhevp text-field" data-testid="TextFieldWrapper__email"><label
      class="BaseWrap-sc-gjQpdd BaseText-ewhhUZ TextFieldLabel-klrYvg iUEiRd stdEm fvoOvz text-field__label text-field__label--single-line" for="newsletter-text-field-email" data-testid="TextFieldLabel__email">
      <div class="TextFieldLabelText-cvvxBl eeDYTb">E-mail address</div>
      <div class="TextFieldInputContainer-jcMPhb oFrOs"><input aria-describedby="privacy-text" aria-invalid="false" id="newsletter-text-field-email" required="" name="email" placeholder="E-mail address"
          class="BaseInput-fAzTdK TextFieldControlInput-eFUxkf eGzzTT lglhcT text-field__control text-field__control--input" type="email" data-testid="TextFieldInput__email" value=""></div>
    </label><button class="BaseButton-bLlsy ButtonWrapper-xCepQ bqVKKv dwpimO button button--utility TextFieldButton-csBrgY edxbrw" data-event-click="{&quot;element&quot;:&quot;Button&quot;}" data-testid="Button" type="submit"><span
        class="ButtonLabel-cjAuJN hzwRuG button__label">Sign up</span></button></span>
  <div id="privacy-text" tabindex="-1" class="NewsletterSubscribeFormDisclaimer-bTVtiV bsgLJZ"><span>
      <p>By signing up, you agree to our <a href="https://www.condenast.com/user-agreement" rel="nofollow noopener noreferrer" target="_blank">User Agreement</a> and
        <a href="https://www.condenast.com/privacy-policy" rel="nofollow noopener noreferrer" target="_blank">Privacy Policy &amp; Cookie Statement</a>. This site is protected by reCAPTCHA and the
        Google<a href="https://policies.google.com/privacy" rel="nofollow noopener noreferrer" target="_blank"> Privacy Policy</a> and<a href="https://policies.google.com/terms" rel="nofollow noopener noreferrer" target="_blank"> Terms of Service</a>
        apply.</p>
    </span></div>
</form>

Text Content

Skip to main content

Get 12 weeks for $29.99 $6

 * Newsletter

Story Saved

To revisit this article, select My Account, then View saved stories

Close Alert

Sign In
Subscribe
Limited-time offer.
Get 12 weeks for $29.99 $6, plus a free tote.
Subscribe
Cancel anytime.


Search
Search
 * News
 * Books & Culture
 * Fiction & Poetry
 * Humor & Cartoons
 * Magazine
 * Puzzles & Games
 * Video
 * Podcasts
 * Goings On
 * Festival
 * Shop

Open Navigation Menu
Menu
Story Saved

To revisit this article, visit My Profile, then View saved stories

Close Alert




Limited-time offer. Limited-time offer.

Subscribe to The New Yorker for $29.99 $6, plus get a free tote. Cancel anytime.

Subscribe to The New Yorker for $29.99 $6, plus get a free tote. Cancel anytime.

Subscribe Subscribe Subscribe Already a subscriber? Sign in
Become a New Yorker subscriber. 12 weeks for $29.99 $6, plus get a free tote.
Cancel anytime. Become a New Yorker subscriber.
12 weeks for $29.99 $6. Subscribe now Subscribe now Subscribe now



Annals of Artificial Intelligence


WILL A.I. BECOME THE NEW MCKINSEY?

As it’s currently imagined, the technology promises to concentrate wealth and
disempower workers. Is an alternative possible?

By Ted Chiang

May 4, 2023

Illustration by Berke Yazicioglu
Save this storySave this story
Save this storySave this story

When we talk about artificial intelligence, we rely on metaphor, as we always do
when dealing with something new and unfamiliar. Metaphors are, by their nature,
imperfect, but we still need to choose them carefully, because bad ones can lead
us astray. For example, it’s become very common to compare powerful A.I.s to
genies in fairy tales. The metaphor is meant to highlight the difficulty of
making powerful entities obey your commands; the computer scientist Stuart
Russell has cited the parable of King Midas, who demanded that everything he
touched turn into gold, to illustrate the dangers of an A.I. doing what you tell
it to do instead of what you want it to do. There are multiple problems with
this metaphor, but one of them is that it derives the wrong lessons from the
tale to which it refers. The point of the Midas parable is that greed will
destroy you, and that the pursuit of wealth will cost you everything that is
truly important. If your reading of the parable is that, when you are granted a
wish by the gods, you should phrase your wish very, very carefully, then you
have missed the point.

So, I would like to propose another metaphor for the risks of artificial
intelligence. I suggest that we think about A.I. as a management-consulting
firm, along the lines of McKinsey & Company. Firms like McKinsey are hired for a
wide variety of reasons, and A.I. systems are used for many reasons, too. But
the similarities between McKinsey—a consulting firm that works with ninety per
cent of the Fortune 100—and A.I. are also clear. Social-media companies use
machine learning to keep users glued to their feeds. In a similar way, Purdue
Pharma used McKinsey to figure out how to “turbocharge” sales of OxyContin
during the opioid epidemic. Just as A.I. promises to offer managers a cheap
replacement for human workers, so McKinsey and similar firms helped normalize
the practice of mass layoffs as a way of increasing stock prices and executive
compensation, contributing to the destruction of the middle class in America.

A former McKinsey employee has described the company as “capital’s willing
executioners”: if you want something done but don’t want to get your hands
dirty, McKinsey will do it for you. That escape from accountability is one of
the most valuable services that management consultancies provide. Bosses have
certain goals, but don’t want to be blamed for doing what’s necessary to achieve
those goals; by hiring consultants, management can say that they were just
following independent, expert advice. Even in its current rudimentary form, A.I.
has become a way for a company to evade responsibility by saying that it’s just
doing what “the algorithm” says, even though it was the company that
commissioned the algorithm in the first place.

The question we should be asking is: as A.I. becomes more powerful and flexible,
is there any way to keep it from being another version of McKinsey? The question
is worth considering across different meanings of the term “A.I.” If you think
of A.I. as a broad set of technologies being marketed to companies to help them
cut their costs, the question becomes: how do we keep those technologies from
working as “capital’s willing executioners”? Alternatively, if you imagine A.I.
as a semi-autonomous software program that solves problems that humans ask it to
solve, the question is then: how do we prevent that software from assisting
corporations in ways that make people’s lives worse? Suppose you’ve built a
semi-autonomous A.I. that’s entirely obedient to humans—one that repeatedly
checks to make sure it hasn’t misinterpreted the instructions it has received.
This is the dream of many A.I. researchers. Yet such software could easily still
cause as much harm as McKinsey has.

Note that you cannot simply say that you will build A.I. that only offers
pro-social solutions to the problems you ask it to solve. That’s the equivalent
of saying that you can defuse the threat of McKinsey by starting a consulting
firm that only offers such solutions. The reality is that Fortune 100 companies
will hire McKinsey instead of your pro-social firm, because McKinsey’s solutions
will increase shareholder value more than your firm’s solutions will. It will
always be possible to build A.I. that pursues shareholder value above all else,
and most companies will prefer to use that A.I. instead of one constrained by
your principles.

Is there a way for A.I. to do something other than sharpen the knife blade of
capitalism? Just to be clear, when I refer to capitalism, I’m not talking about
the exchange of goods or services for prices determined by a market, which is a
property of many economic systems. When I refer to capitalism, I’m talking about
a specific relationship between capital and labor, in which private individuals
who have money are able to profit off the effort of others. So, in the context
of this discussion, whenever I criticize capitalism, I’m not criticizing the
idea of selling things; I’m criticizing the idea that people who have lots of
money get to wield power over people who actually work. And, more specifically,
I’m criticizing the ever-growing concentration of wealth among an ever-smaller
number of people, which may or may not be an intrinsic property of capitalism
but which absolutely characterizes capitalism as it is practiced today.



As it is currently deployed, A.I. often amounts to an effort to analyze a task
that human beings perform and figure out a way to replace the human being.
Coincidentally, this is exactly the type of problem that management wants
solved. As a result, A.I. assists capital at the expense of labor. There isn’t
really anything like a labor-consulting firm that furthers the interests of
workers. Is it possible for A.I. to take on that role? Can A.I. do anything to
assist workers instead of management?



Some might say that it’s not the job of A.I. to oppose capitalism. That may be
true, but it’s not the job of A.I. to strengthen capitalism, either. Yet that is
what it currently does. If we cannot come up with ways for A.I. to reduce the
concentration of wealth, then I’d say it’s hard to argue that A.I. is a neutral
technology, let alone a beneficial one.

Many people think that A.I. will create more unemployment, and bring up
universal basic income, or U.B.I., as a solution to that problem. In general, I
like the idea of universal basic income; however, over time, I’ve become
skeptical about the way that people who work in A.I. suggest U.B.I. as a
response to A.I.-driven unemployment. It would be different if we already had
universal basic income, but we don’t, so expressing support for it seems like a
way for the people developing A.I. to pass the buck to the government. In
effect, they are intensifying the problems that capitalism creates with the
expectation that, when those problems become bad enough, the government will
have no choice but to step in. As a strategy for making the world a better
place, this seems dubious.

You may remember that, in the run-up to the 2016 election, the actress Susan
Sarandon—who was a fervent supporter of Bernie Sanders—said that voting for
Donald Trump would be better than voting for Hillary Clinton because it would
bring about the revolution more quickly. I don’t know how deeply Sarandon had
thought this through, but the Slovenian philosopher Slavoj Žižek said the same
thing, and I’m pretty sure he had given a lot of thought to the matter. He
argued that Trump’s election would be such a shock to the system that it would
bring about change.

Video From The New Yorker

Surfing on Kelly Slater’s Machine-Made Wave



What Žižek advocated for is an example of an idea in political philosophy known
as accelerationism. There are a lot of different versions of accelerationism,
but the common thread uniting left-wing accelerationists is the notion that the
only way to make things better is to make things worse. Accelerationism says
that it’s futile to try to oppose or reform capitalism; instead, we have to
exacerbate capitalism’s worst tendencies until the entire system breaks down.
The only way to move beyond capitalism is to stomp on the gas pedal of
neoliberalism until the engine explodes.

I suppose this is one way to bring about a better world, but, if it’s the
approach that the A.I. industry is adopting, I want to make sure everyone is
clear about what they’re working toward. By building A.I. to do jobs previously
performed by people, A.I. researchers are increasing the concentration of wealth
to such extreme levels that the only way to avoid societal collapse is for the
government to step in. Intentionally or not, this is very similar to voting for
Trump with the goal of bringing about a better world. And the rise of Trump
illustrates the risks of pursuing accelerationism as a strategy: things can get
very bad, and stay very bad for a long time, before they get better. In fact,
you have no idea of how long it will take for things to get better; all you can
be sure of is that there will be significant pain and suffering in the short and
medium term.

I’m not very convinced by claims that A.I. poses a danger to humanity because it
might develop goals of its own and prevent us from turning it off. However, I do
think that A.I. is dangerous inasmuch as it increases the power of capitalism.
The doomsday scenario is not a manufacturing A.I. transforming the entire planet
into paper clips, as one famous thought experiment has imagined. It’s
A.I.-supercharged corporations destroying the environment and the working class
in their pursuit of shareholder value. Capitalism is the machine that will do
whatever it takes to prevent us from turning it off, and the most successful
weapon in its arsenal has been its campaign to prevent us from considering any
alternatives.

People who criticize new technologies are sometimes called Luddites, but it’s
helpful to clarify what the Luddites actually wanted. The main thing they were
protesting was the fact that their wages were falling at the same time that
factory owners’ profits were increasing, along with food prices. They were also
protesting unsafe working conditions, the use of child labor, and the sale of
shoddy goods that discredited the entire textile industry. The Luddites did not
indiscriminately destroy machines; if a machine’s owner paid his workers well,
they left it alone. The Luddites were not anti-technology; what they wanted was
economic justice. They destroyed machinery as a way to get factory owners’
attention. The fact that the word “Luddite” is now used as an insult, a way of
calling someone irrational and ignorant, is a result of a smear campaign by the
forces of capital.

Whenever anyone accuses anyone else of being a Luddite, it’s worth asking, is
the person being accused actually against technology? Or are they in favor of
economic justice? And is the person making the accusation actually in favor of
improving people’s lives? Or are they just trying to increase the private
accumulation of capital?



Today, we find ourselves in a situation in which technology has become conflated
with capitalism, which has in turn become conflated with the very notion of
progress. If you try to criticize capitalism, you are accused of opposing both
technology and progress. But what does progress even mean, if it doesn’t include
better lives for people who work? What is the point of greater efficiency, if
the money being saved isn’t going anywhere except into shareholders’ bank
accounts? We should all strive to be Luddites, because we should all be more
concerned with economic justice than with increasing the private accumulation of
capital. We need to be able to criticize harmful uses of technology—and those
include uses that benefit shareholders over workers—without being described as
opponents of technology.



Imagine an idealized future, a hundred years from now, in which no one is forced
to work at any job they dislike, and everyone can spend their time on whatever
they find most personally fulfilling. Obviously it’s hard to see how we’d get
there from here. But now consider two possible scenarios for the next few
decades. In one, management and the forces of capital are even more powerful
than they are now. In the other, labor is more powerful than it is now. Which
one of these seems more likely to get us closer to that idealized future? And,
as it’s currently deployed, which one is A.I. pushing us toward?

Of course, there is the argument that new technology improves our standard of
living in the long term, which makes up for the unemployment that it creates in
the short term. This argument carried weight for much of the post-Industrial
Revolution period, but it has lost its force in the past half century. In the
United States, per-capita G.D.P. has almost doubled since 1980, while the median
household income has lagged far behind. That period covers the
information-technology revolution. This means that the economic value created by
the personal computer and the Internet has mostly served to increase the wealth
of the top one per cent of the top one per cent, instead of raising the standard
of living for U.S. citizens as a whole.

Of course, we all have the Internet now, and the Internet is amazing. But
real-estate prices, college tuition, and health-care costs have all risen faster
than inflation. In 1980, it was common to support a family on a single income;
now it’s rare. So, how much progress have we really made in the past forty
years? Sure, shopping online is fast and easy, and streaming movies at home is
cool, but I think a lot of people would willingly trade those conveniences for
the ability to own their own homes, send their kids to college without running
up lifelong debt, and go to the hospital without falling into bankruptcy. It’s
not technology’s fault that the median income hasn’t kept pace with per-capita
G.D.P.; it’s mostly the fault of Ronald Reagan and Milton Friedman. But some
responsibility also falls on the management policies of C.E.O.s like Jack Welch,
who ran General Electric between 1981 and 2001, as well as on consulting firms
like McKinsey. I’m not blaming the personal computer for the rise in wealth
inequality—I’m just saying that the claim that better technology will
necessarily improve people’s standard of living is no longer credible.

The fact that personal computers didn’t raise the median income is particularly
relevant when thinking about the possible benefits of A.I. It’s often suggested
that researchers should focus on ways that A.I. can increase individual workers’
productivity rather than replace them; this is referred to as the augmentation
path, as opposed to the automation path. That’s a worthy goal, but, by itself,
it won’t improve people’s economic fortunes. The productivity software that ran
on personal computers was a perfect example of augmentation rather than
automation: word-processing programs replaced typewriters rather than typists,
and spreadsheet programs replaced paper spreadsheets rather than accountants.
But the increased personal productivity brought about by the personal computer
wasn’t matched by an increased standard of living.

The only way that technology can boost the standard of living is if there are
economic policies in place to distribute the benefits of technology
appropriately. We haven’t had those policies for the past forty years, and,
unless we get them, there is no reason to think that forthcoming advances in
A.I. will raise the median income, even if we’re able to devise ways for it to
augment individual workers. A.I. will certainly reduce labor costs and increase
profits for corporations, but that is entirely different from improving our
standard of living.

It would be convenient if we could assume that a utopian future is right around
the corner and develop technology for use in that future. But the fact that a
given technology would be helpful in a utopia does not imply that it’s helpful
now. In a utopia where there’s a machine that converts toxic waste into food,
generating toxic waste wouldn’t be a problem, but, in the here and now, no one
could claim that generating toxic waste is harmless. Accelerationists might
argue that generating more toxic waste will motivate the invention of a
waste-to-food converter, but how convincing is that? We evaluate the
environmental impact of technologies in the context of the mitigations that are
currently available, not in the context of hypothetical future mitigations. By
the same token, we can’t evaluate A.I. by imagining how helpful it will be in a
world with U.B.I.; we have to evaluate it in light of the existing imbalance
between capital and labor, and, in that context, A.I. is a threat because of the
way it assists capital.

A former partner at McKinsey defended the company’s actions by saying, “We don’t
do policy. We do execution.” But this is a pretty thin excuse; harmful policy
decisions are more likely to be made when consulting firms—or new
technologies—offer ways to implement them. The version of A.I. that’s currently
being developed makes it easier for companies to lay people off. So is there any
way to develop a kind of A.I. that makes it harder?

In his book “How to Be an Anticapitalist in the 21st Century,” the sociologist
Erik Olin Wright offers a taxonomy of strategies for responding to the harms of
capitalism. Two of the strategies he mentions are smashing capitalism and
dismantling capitalism, which probably fall outside the scope of this
discussion. The ones that are more relevant here are taming capitalism and
resisting capitalism. Roughly speaking, taming capitalism means government
regulation, and resisting capitalism means grassroots activism and labor unions.
Are there ways for A.I. to strengthen those things? Is there a way for A.I. to
empower labor unions or worker-owned coöperatives?

In 1976, the workers at the Lucas Aerospace Corporation in Birmingham, England,
were facing layoffs because of cuts in defense spending. In response, the shop
stewards produced a document known as the Lucas Plan, which described a hundred
and fifty “socially useful products,” ranging from dialysis machines to wind
turbines and hybrid engines for cars, that the workforce could build with its
existing skills and equipment rather than being laid off. The management at
Lucas Aerospace rejected the proposal, but it remains a notable modern example
of workers trying to steer capitalism in a more human direction. Surely
something similar must be possible with modern computing technology.



Does capitalism have to be as harmful as it currently is? Maybe not. The three
decades following the Second World War are sometimes known as the golden age of
capitalism. This period was partially the result of better government policies,
but the government didn’t create the golden age on its own: corporate culture
was different during this era. In General Electric’s annual report from 1953,
the company bragged about how much it paid in taxes and how much it was spending
on payroll. It explicitly said that “maximizing employment security is a prime
company goal.” The founder of Johnson & Johnson said that the company’s
responsibility to its employees was higher than its responsibility to its
shareholders. Corporations then had a radically different conception of their
role in society compared with corporations today.

Is there a way to get back to those values? It seems unlikely, but remember that
the golden age of capitalism came after the enormous wealth inequality of the
Gilded Age. Right now we’re living in a second Gilded Age, in which wealth
inequality is about the same as it was back in 1913, so it’s not impossible that
we could go from where we are now to a second golden age. Of course, in between
the first Gilded Age and the golden age we had the Great Depression and two
World Wars. An accelerationist might say that those events were necessary to
bring about the golden age, but I think most of us would prefer to skip over
those steps. The task before us is to imagine ways for technology to move us
toward a golden age without bringing about another Great Depression first.

We all live in a capitalist system, so we are all participants in capitalism
whether we like it or not. And it’s reasonable to wonder if there’s anything you
as an individual can do. If you work as a food scientist at Frito-Lay and your
job is to invent new flavors of potato chip, I’m not going to say that you have
an ethical obligation to quit because you’re assisting the engine of
consumerism. You’re using your training as a food scientist to provide customers
with a pleasant experience; that’s a perfectly reasonable way to make a living.

But many of the people who work in A.I. regard it as more important than
inventing new flavors of potato chip. They say it’s a world-changing technology.
If that’s the case, then they have a duty to find ways for A.I. to make the
world better without first making it worse. Can A.I. ameliorate the inequities
of our world other than by pushing us to the brink of societal collapse? If A.I.
is as powerful a tool as its proponents claim, they should be able to find other
uses for it besides intensifying the ruthlessness of capital.



If there is any lesson that we should take from stories about genies granting
wishes, it’s that the desire to get something without effort is the real
problem. Think about the story of “The Sorcerer’s Apprentice,” in which the
apprentice casts a spell to make broomsticks carry water but is unable to make
them stop. The lesson of that story is not that magic is impossible to control:
at the end of the story, the sorcerer comes back and immediately fixes the mess
the apprentice made. The lesson is that you can’t get out of doing the hard
work. The apprentice wanted to avoid his chores, and looking for a shortcut was
what got him into trouble.

The tendency to think of A.I. as a magical problem solver is indicative of a
desire to avoid the hard work that building a better world requires. That hard
work will involve things like addressing wealth inequality and taming
capitalism. For technologists, the hardest work of all—the task that they most
want to avoid—will be questioning the assumption that more technology is always
better, and the belief that they can continue with business as usual and
everything will simply work itself out. No one enjoys thinking about their
complicity in the injustices of the world, but it is imperative that the people
who are building world-shaking technologies engage in this kind of critical
self-examination. It’s their willingness to look unflinchingly at their own role
in the system that will determine whether A.I. leads to a better world or a
worse one. ♦





MORE SCIENCE AND TECHNOLOGY

 * Can we stop runaway A.I.?

 * Saving the climate will depend on blue-collar workers. Can we train enough of
   them before time runs out?

 * There are ways of controlling A.I.—but first we need to stop mythologizing
   it.

 * A security camera for the entire planet.

 * What’s the point of reading writing by humans?

 * A heat shield for the most important ice on Earth.

 * The climate solutions we can’t live without.

Sign up for our daily newsletter to receive the best stories from The New
Yorker.

Ted Chiang is the author of two collections of science-fiction short stories,
“Stories of Your Life and Others,” and “Exhalation.”

More:TechnologyArtificial IntelligenceMachine LearningCapitalismEconomicsLabor


DAILY

The best of The New Yorker today, including essential reads, fiction, humor, and
a peek behind the scenes of our biggest stories.
E-mail address

Sign up

By signing up, you agree to our User Agreement and Privacy Policy & Cookie
Statement. This site is protected by reCAPTCHA and the Google Privacy Policy and
Terms of Service apply.



Read More
A Reporter at Large
The Titan Submersible Was “an Accident Waiting to Happen”
Interviews and e-mails with expedition leaders and employees reveal how
OceanGate ignored desperate warnings from inside and outside the company. “It’s
a lemon,” one wrote.

By Ben Taub

Profiles
Why Sarah Jessica Parker Keeps Playing Carrie Bradshaw
In all of her professional endeavors, including the “Sex and the City”
franchise, Parker considers herself a “bitter ender.”

By Rachel Syme

Annals of Medicine
What We Still Don’t Understand About Postpartum Psychosis
The recent tragedy surrounding Lindsay Clancy and her children underscores
popular misconceptions about a grave and mysterious disorder.

By Jessica Winter

Life and Letters
The Novelist Whose Inventions Went Too Far
After the Afro-Cuban writer H. G. Carrillo died, his husband learned that almost
everything the writer had shared about his life was made up—including his Cuban
identity.

By D. T. Max






Limited-time offer.
Get 12 weeks for $29.99 $6, plus a free tote. Subscribe Cancel anytime.
Get 12 weeks for $29.99 $6, plus a free tote. Subscribe Cancel anytime.

Sections

 * News
 * Books & Culture
 * Fiction & Poetry
 * Humor & Cartoons
 * Magazine
 * Crossword
 * Video
 * Podcasts
 * Archive
 * Goings On

More

 * Customer Care
 * Shop The New Yorker
 * Buy Covers and Cartoons
 * Condé Nast Store
 * Digital Access
 * Newsletters
 * Jigsaw Puzzle
 * RSS

 * About
 * Careers
 * Contact
 * F.A.Q.
 * Media Kit
 * Press
 * Accessibility Help

© 2023 Condé Nast. All rights reserved. Use of this site constitutes acceptance
of our User Agreement and Privacy Policy and Cookie Statement and Your
California Privacy Rights. The New Yorker may earn a portion of sales from
products that are purchased through our site as part of our Affiliate
Partnerships with retailers. The material on this site may not be reproduced,
distributed, transmitted, cached or otherwise used, except with the prior
written permission of Condé Nast. Ad Choices


 * Facebook
 * Twitter
 * Snapchat
 * YouTube
 * Instagram


Manage Preferences




We and our partners store and/or access information on a device, such as unique
IDs in cookies to process personal data. You may accept or manage your choices
by clicking below or at any time in the privacy policy page. These choices will
be signaled to our partners and will not affect browsing data.More Information


WE AND OUR PARTNERS PROCESS DATA TO PROVIDE:

Use precise geolocation data. Actively scan device characteristics for
identification. Store and/or access information on a device. Personalised ads
and content, ad and content measurement, audience insights and product
development. List of Partners (vendors)

I Accept
Show Purposes