By Kevin D. Williamson
Thursday, September 29, 2016
The arresting headline — “U.S. Department of Labor sues
Palantir for racial discrimination” — could have gone with two very different
stories.
The first possible story, the obvious and boring one,
turns out, alas, to be the operative one: The Obama administration is going
after Palantir Technologies, a “big data” concern started by Silicon Valley
entrepreneur Peter Thiel, on the grounds that it discriminated against
Asian-American job applicants. The case is risible, resting on an absurdly
small data set (three job descriptions and 21 hiring decisions) and the
assumption that applicants for highly specialized positions in one of the
world’s most esoteric technology companies are interchangeable widgets.
It is less than obvious that Asian Americans have been
shut out of technology jobs in Silicon Valley, but, by all means, let us
consider the question.
Palantir says that the government is engaged in “flawed
statistical analysis.” It seems more likely that the Obama administration is
engaged in straightforward political retribution and intimidation: Peter Thiel
is an increasingly vocal Republican activist who spoke on Donald Trump’s behalf
at this year’s Republican National Convention. (In the interest of disclosure,
I should note that he is a contributor, both editorially and financially, to National Review.) Democrats prefer being
opposed, if they must be opposed at all, by southern biblioplangists who lend
themselves to caricature; cerebral California technology billionaires, on the
other hand, are the kind of opposition they could do without, hence the desire
to make examples of those who step out of line. Given the current
administration’s long,
nasty, and criminal history of using agencies of the federal government to
go after political enemies, that seems a perfectly reasonable explanation.
(Of course, it is just barely possible that the
administration’s motives here are innocent; one of the problems with political
corruption is that it casts suspicion on any action that might reasonably be
interpreted as corrupt, which is one of the reasons why Lois Lerner and John
Koskinen should be in a federal penitentiary.)
Thiel, who has shown a flair for litigation lately (he
financed Hulk Hogan’s invasion-of-privacy lawsuit against Gawker), probably will be able to manage this conflict with the
Labor Department, though the increasingly open tendency of Democrats to weaponize
federal agencies and prosecutors’ offices (ask True the Vote, Kay Bailey
Hutchison, Tom DeLay, Rick Perry, the Competitive Enterprise Institute, Exxon,
the pastors of Houston . . . ) makes one wonder why any large and complex
business concern would willingly submit to American jurisdiction when it might
as easily incorporate in a country with more honest and transparent public
institutions, such as Canada or Switzerland.
But what about the other possible interpretation of that
headline? When I read it, I thought for a second that it might mean not that
Palantir Technologies Inc. was accused of discrimination but that Palantir
itself stood so accused.
Palantir is an artificial-intelligence platform. There
are many versions of it operating around the world: The federal government uses
it to track down financial criminals and, if the whispers are to be credited,
sundry terrorists camped out in the dusty corners of Jihadistan. Hedge funds
use it for their own purposes. Information Warfare Monitor used it to uncover
the GhostNet in China. It was used to help organize relief efforts after
Hurricane Sandy. It is, to say the least, an interesting piece of technology.
But the science-fiction stuff — artificial intelligence,
machine-learning systems, neural networks, all that cool-sounding innovation —
already is working its way into the much more quotidian aspects of life,
particularly in areas such as actuarial analysis and credit. This presents an
interesting problem, as Clive Thompson writes in the current edition of Wired: Once a sophisticated neural
network is up and running, it “learns” by processing massive amounts of data,
and its decision-making processes are opaque, even to the people who designed
it. It is a “black box,” a very, very black one, in fact. “Ask its creator how
it achieves a certain result and you’ll likely get a shrug,” he writes.
This will affect ordinary people in predictable ways.
Thompson considers the case of a homeowner being denied property insurance.
Today, that denial could be explained by any number of financial or geographic
factors, but systems such as Palantir are useful in part because they detect
relationships that are not obvious to humans, or that are counterintuitive. It
is not only possible but likely that such systems will produce results that are
discomfiting in some quarters. It is not difficult to imagine that they will
produce substantial disparities in health-insurance prices, mortgage rates,
consumer-credit offers, and the like, and that those disparities will follow
demographic cleavages that are politically sensitive. Wider use of such
decision-making processes — say, in screening job applicants or making
admissions decisions at public universities — will produce new and knotty
problems.
The European Union has passed a law entitling consumers
to explanations of how financial institutions make decisions about them, but
those explanations may turn out to require advanced study at MIT.
What should we think about such opaque decision-making
processes?
We should begin with the three most important words in
public policy: “Compared to what?” Black-box systems are likely to prove
superior to our current model — nerds with actuarial tables — and may be less
biased. Bias in actuarial methodology is a longstanding problem in the field,
and a subject of intense study by its experts. The problem with black-box
systems is less likely to be their propagating bias but their revealing it.
To take one example, African-American men are
shorter-lived than the average American man, and than white men.
African-American men also suffer from certain health problems at much higher
rates: Their rate of diabetes, for example, is 70 percent higher than that of
whites. There is an interesting legal and political history to how these
realities (and, in some cases, racial fictions) have been incorporated into
insurance pricing. But the trend has been very strongly against that kind of
discrimination, to say the least. Indeed, one of the baffling features of the
so-called Affordable Care Act is its insistence that insurance companies may
not “discriminate against” people with pre-existing conditions, as though it
were logically possible to insure against events in the past any more than one
could go to Vegas and place a bet on last year’s Super Bowl.
Credit scores and income statements are pretty blunt
tools, as are many of the instruments used in calculating insurance premiums.
What is likely to emerge from black-box systems is not a recapitulation of
decisions based on gross racial categories but highly sophisticated and highly
individualistic analyses that nonetheless produce results that are, for lack of
a better word, discriminatory, though whether a machine can engage in racial
discrimination properly understood is a philosophical question. No one doubts
that an effective system for screening terrorism threats would spotlight more
people from Kandahar than from Helsinki. What we sometimes denounce as
“profiling” may be useful or not, depending — we always seem to overlook this
part — on what is in the profile and how it is constructed. A badly designed
profile might propagate bias; a well-designed one might reveal underlying
social realities about which we would prefer not to think too much.
It is safe to predict that the Department of Labor is not
thinking very much about this problem just yet.
Most of our large pieces of policy architecture date from
the New Deal and the Great Society, from that enormous boom in managerial
thinking that characterized mid-century America, whose faith in free markets
had been shaken by a misunderstood Great Depression and whose faith in
government expertise had been inflated by a misunderstood war effort. Most of
our political obsessions date from that period, too.
That’s one reason why, for example, our discussions about
the condition of black America mainly fail to take into account that the
emergence of affluent and prosperous African and Caribbean immigrant
communities has complicated what it means to be African American, that clumsy
slogans like “Black Lives Matter” fail to account for the fact that the lives
of Nigerian-American financiers in Menlo Park are not very much like those of
people in East St. Louis. It is why our response to the problem of weak wage
growth for low-skilled jobs is so hilariously crude: “Just pass a law saying
McDonald’s has got to pay ’em more!” Our being mentally fixed somewhere between
1957 and 1964 prevents us from thinking intelligently about things such as the
economy, trade, public pensions, entitlements, national security, and
education, much less about the fact that in only a few years the question in
discrimination claims will not be “Discrimination by whom?” but “Discrimination by what?”
And what have we seen from the Obama administration,
which promised to be forward-looking and evidence-driven? Mainly a return to
the most low-tech approach to public policy there is: the enemies list.
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