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?????????????????????????????????????????????????Monday, July 17, 2017
How Do We Stop the Internet from Lying to Us?
Algorithms can dictate whether you get a mortgage or how much you pay for insurance. But sometimes they’re wrong.
Photo Credit: whiteMocca / Shutterstock.com
Lots of algorithms go bad unintentionally. Some of them, however, are
made to be criminal. Algorithms are formal rules, usually written in
computer code, that make predictions on future events based on
historical patterns. To train an algorithm you need to provide
historical data as well as a definition of success.
We’ve seen finance get taken over by algorithms in the past few decades.
Trading algorithms use historical data to predict movements in the
market. Success for that algorithm is a predictable market move, and the
algorithm is vigilant for patterns that have historically happened just
before that move. Financial risk models also use historical market
changes to predict cataclysmic events in a more global sense, so not for
an individual stock but rather for an entire market. The risk model for
mortgage-backed securities was famously bad – intentionally so – and
the trust in those models can be blamed for much of the scale and subsequent damage wrought by the 2008 financial crisis.
Since 2008, we’ve heard less from algorithms in finance, and much more
from big data algorithms. The target of this new generation of
algorithms has been shifted from abstract markets to individuals. But
the underlying functionality is the same: collect historical data about
people, profiling their behaviour online, location, or answers to
questionnaires, and use that massive dataset to predict their future
purchases, voting behaviour, or work ethic.
The recent proliferation in big data models has gone largely unnoticed
by the average person, but it’s safe to say that most important moments
where people interact with large bureaucratic systems now involve an
algorithm in the form of a scoring system. Getting into college, getting
a job, being assessed as a worker, getting a credit card or insurance,
voting, and even policing are
in many cases done algorithmically. Moreover, the technology introduced
into these systematic decisions is largely opaque, even to their
creators, and has so far largely escaped meaningful regulation, even
when it fails. That makes the question of which of these algorithms are
working on our behalf even more important and urgent.
I have a four-layer hierarchy when it comes to bad algorithms. At the
top there are the unintentional problems that reflect cultural biases.
For example, when Harvard professor Latanya Sweeney found that Google
searches for names perceived to be black generated ads associated with criminal activity,
we can assume that there was no Google engineer writing racist code. In
fact, the ads were trained to be bad by previous users of Google
search, who were more likely to click on a criminal records ad when they
searched for a black sounding name. Another example: the Google image search result for “unprofessional hair”,which
returned almost exclusively black women, is similarly trained by the
people posting or clicking on search results throughout time.
One layer down we come to algorithms that go bad through neglect. These
would include scheduling programs that prevent people who work minimum
wage jobs from leading decent lives. The algorithms treat them like cogs
in a machine, sending them to work at different times of the day and on
different days each week, preventing them from having regular
childcare, a second job, or going to night school. They are brutally
efficient, hugely scaled, and largely legal, collecting pennies on the
backs of workers. Or consider Google’s system for automatically tagging
photos. It had a consistent problem whereby black people were being labelled gorillas.
This represents neglect of a different nature, namely quality
assessment of the product itself: they didn’t check that it worked on a
wide variety of test cases before releasing the code.
The third layer consists of nasty but legal algorithms. For example, there were Facebook executives in Australia showing advertisers ways
to find and target vulnerable teenagers. Awful but probably not
explicitly illegal. Indeed online advertising in general can be seen as a
spectrum, where on the one hand the wealthy are presented with luxury
goods to buy but the poor and desperate are preyed upon by online payday
lenders. Algorithms charge people more for car insurance if they don’t
seem likely to comparison shop and Uber just halted an algorithm it was using to predict how low an offer of pay could be, thereby reinforcing the gender pay gap.
Finally, there’s the bottom layer, which consists of intentionally
nefarious and sometimes outright illegal algorithms. There are hundreds
of private companies, including dozens in the UK, that offer mass
surveillance tools. They are marketed as a way of locating terrorists or
criminals, but they can be used to target and root out citizen
activists. And because they collect massive amounts of data, predictive
algorithms and scoring systems are used to filter out the signal from
the noise. The illegality of this industry is under debate, but a recent undercover operation by journalists at Al Jazeera has
exposed the relative ease with which middlemen representing repressive
regimes in Iran and South Sudan have been able to buy such systems. For
that matter, observers have criticised China’s social credit scoring system.
Called “Sesame Credit,” it’s billed as mostly a credit score, but it
may also function as a way of keeping tabs on an individual’s political
opinions, and for that matter as a way of nudging people towards
compliance.
Closer to home, there’s Uber’s “Greyball,” an
algorithm invented specifically to avoid detection when the taxi
service is functioning illegally in a city. It used data to predict
which riders were violating the terms of service of Uber, or which
riders were undercover government officials. Telltale signs that
Greyball picked up included multiple use of the app in a single day and
using a credit card tied to a police union.
The most famous malicious and illegal algorithm we’ve discovered so far
is the one used by Volkswagen in 11 million vehicles worldwide to deceive the emissions tests,
and in particular to hide the fact that the vehicles were emitting
nitrogen oxide at up to 35 times the levels permitted by law. And
although it seemed simply like a devious device, this qualifies as an
algorithm as well. It was trained to identify and predict testing
conditions versus road conditions, and to function differently depending
on that result. And, like Greyball, it was designed to deceive.
It’s worth dwelling on the example of car manufacturers because the
world of algorithms – a very young, highly risky new industry with no
safety precautions in place – is rather like the early car industry.
With its naive and exuberant faith in its own technology, the world of
AI is selling the equivalent of cars without bumpers whose wheels might
fall off at any moment. And I’m sure there were such cars made once upon
a time, but over time, as we saw more damage being done by faulty
design, we came up with more rules to protect passengers and
pedestrians. So, what can we learn from the current, mature world of car
makers in the context of illegal software?
First, similar types of software are being deployed by other car
manufacturers that turn off emissions controls in certain settings. In
other words, this was not a situation in which there was only one bad
actor, but rather a standard operating procedure. Moreover, we can
assume this doesn’t represent collusion, but rather a simple case of
extreme incentives combined with a calculated low probability of getting
caught on the part of the car manufacturers. It’s reasonable to expect,
then, that there are plenty of other algorithms being used to skirt
rules and regulations deemed too expensive, especially when the builders
of the algorithms remain smug about their chances.
Next, the VW cheating started in 2009, which means it went undetected
for five years. What else has been going on for five years? This line of
thinking makes us start looking around, wondering which companies are
currently hoodwinking regulators, evading privacy laws, or committing
algorithmic fraud with impunity.
Indeed it might seem like a slam dunk business model, in terms of
cost-benefit analysis: cheat until regulators catch up with us, if they
ever do, and then pay a limited fine that doesn’t make much of a dent in
our cumulative profit. That’s how it worked in the aftermath of the
financial crisis, after all. In the name of shareholder value, we might
be obliged to do this.
Put it another way. We’re all expecting cars to be self-driving in a few years or
a couple of decades at most. When that happens, can we expect there to
be international agreements on what the embedded self-driving car ethics
will look like? Or will pedestrians be at the mercy of the car
manufacturers to decide what happens in the case of an unexpected
pothole? If we get rules, will the rules differ by country, or even by
the country of the manufacturer?
If this sounds confusing for something as easy to observe as car
crashes, imagine what’s going on under the hood, in the relatively
obscure world of complex “deep learning” models.
The tools are there already, to be sure. China has recently demonstrated how well facial recognition technology already works – enough to catch jaywalkers and toilet paper thieves.
That means there are plenty of opportunities for companies to perform
devious tricks on customers or potential hires. For that matter, the
incentives are also in place. Just last month Google was fined €2.4bn for
unfairly placing its own shopping search results in a more prominent
place than its competitors. A similar complaint was levelled at Amazon
by ProPublica last year with respect to its pricing algorithm,
namely that it was privileging its own, in-house products – even when
they weren’t a better deal – over those outside its marketplace. If you
think of the internet as a place where big data companies vie for your
attention, then we can imagine more algorithms like this in our future.
There’s a final parallel to draw with the VW scandal. Namely, the discrepancy in emissions was finally discovered in 2014 by a team of professors and students at West Virginia University,
who applied and received a measly grant of $50,000 from the
International Council on Clean Transportation, an independent nonprofit
organisation paid for by US taxpayers. They spent their money driving
cars around the country and capturing the emissions, a cheap and
straightforward test.
What organisation will put a stop to the oncoming crop of illegal
algorithms? What is the analogue of the International Council on Clean
Transportation? Does there yet exist an organisation that has the
capacity, interest, and ability to put an end to illegal algorithms, and
to prove that these algorithms are harmful? The answer is, so far, no.
Instead, at least in the US, a disparate group of federal agencies is in
charge of enforcing laws in their industry or domain, none of which is
particularly on top of the complex world of big data algorithms.
Elsewhere, the European commission seems to be looking into Google’s
antitrust activity, and Facebook’s fake news problems, but that leaves
multiple industries untouched by scrutiny.
Even more to the point, though, is the question of how involved the
investigation of algorithms would have to be. The current nature of
algorithms is secret, proprietary code, protected as the “secret sauce”
of corporations. They’re so secret that most online scoring systems
aren’t even apparent to the people targeted by them. That means those
people also don’t know the score they’ve been given, nor can they
complain about or contest those scores. Most important, they typically
won’t know if something unfair has happened to them.
Given all of this, it’s difficult to imagine oversight for algorithms,
even when they’ve gone wrong and are actively harming people. For that
matter, not all kinds of harm are distinctly measurable in the first
place. One can make the argument that, what with all the fake news
floating around, our democracy has been harmed. But how do you measure
democracy?
That’s not to say there is no hope. After all, by definition, an illegal
algorithm is breaking an actual law that we can point to. There is,
ultimately, someone that should be held accountable for this. The
problem still remains, how will such laws be enforced?
Ben Shneiderman, a computer science professor at the University of
Maryland, proposed the concept of a National Algorithms Safety Board, in
a talk at the Alan Turing Institute. Modelled on the National
Transportation Safety Board, which investigates ground and air traffic
accidents, this body would similarly be charged with investigating harm,
and specifically in deciding who should be held responsible for
algorithmic harm.
This is a good idea. We should investigate problems when we find them,
and it’s good to have a formal process to do so. If it has sufficient
legal power, the board can perhaps get to the bottom of lots of
commonsense issues. But it’s not clear how comprehensive it could be.
Because here’s where the analogy with car makers breaks down: there is
no equivalent of a 30-car pile-up in the world of algorithms. Most of
the harm happens to isolated individuals, separately and silently. A
proliferation of silent and undetectable car crashes is harder to
investigate than when it happens in plain sight.
I’d still maintain there’s hope. One of the miracles of being a data
sceptic in a land of data evangelists is that people are so impressed
with their technology, even when it is unintentionally creating harm,
they openly describe how amazing it is. And the fact that we’ve already
come across quite a few examples of algorithmic harm means that, as
secret and opaque as these algorithms are, they’re eventually going to
be discovered, albeit after they’ve caused a lot of trouble.
What does this mean for the future? First and foremost, we need to start
keeping track. Each criminal algorithm we discover should be seen as a
test case. Do the rule-breakers get into trouble? How much? Are the
rules enforced, and what is the penalty? As we learned after the 2008
financial crisis, a rule is ignored if the penalty for breaking it is
less than the profit pocketed. And that goes double for a broken rule
that is only discovered half the time.
Even once we start building a track record of enforcement, we have
ourselves an arms race. We can soon expect a fully fledged army of
algorithms that skirt laws, that are sophisticated and silent, and that
seek to get around rules and regulations. They will learn from how
others were caught and do it better the next time. In other words, it
will get progressively more difficult to catch them cheating. Our
tactics have to get better over time too.
We can also expect to be told that the big companies are “dealing with
it privately”. This is already happening with respect to fighting
terrorism. We should not trust them when they say this. We need to
create a standard testing framework – a standard definition of harm –
and require that algorithms be submitted for testing. And we cannot do
this only in “test lab conditions,” either, or we will be reconstructing
the VW emissions scandal.
One of the biggest obstacles to this is that Google, Facebook, or for
that matter Amazon, don’t allow testing of multiple personas – or online
profiles – by outside researchers. Since those companies offer tailored
and personalised service, the only way to see what that service looks
like would be to take on the profile of multiple people, but that is not
allowed. Think about that in the context of the VW testing: it would be
like saying research teams could not have control of a car to test its
emissions. We need to demand more access and ongoing monitoring,
especially once we catch them in illegal acts. For that matter, entire
industries, such as algorithms for insurance and hiring, should be
subject to these monitors, not just individual culprits.
It’s time to gird ourselves for a fight. It will eventually be a
technological arms race, but it starts, now, as a political fight. We
need to demand evidence that algorithms with the potential to harm us be
shown to be acting fairly, legally, and consistently. When we find
problems, we need to enforce our laws with sufficiently hefty fines that
companies don’t find it profitable to cheat in the first place. This is
the time to start demanding that the machines work for us, and not the
other way around.