are rightly concerned that Big Tech companies in Silicon Valley have
an outsized influence over the news and opinions we see on social
media platforms and in Google searches. The fact that most of these
platforms originate in some of the most liberal strongholds in the
country — Mountain View, San Francisco, and Menlo Park, California
— raises questions about whether they're either
intentionally or subconsciously putting their oversized thumbs
on the scales to promote points of view they agree with and hide
views they find odious or dangerous. A
study released today from GovPredict shows
than 90 percent of political donations by Alphabet employees went
to Democrats. This news will only amplify fears
that everything we read, see, and hear is being controlled by tech
industry employees with a left-wing political bent.
the first of a series of articles examining the political
preferences of major American companies, GovPredict looked at the
political donations of employees at Alphabet — the parent company of
Google and many of its subsidiaries, including YouTube, Nest,
Google Ventures, Calico, Adsense, Google Ventures, and Verily. The
analysis used Federal Election Commission (FEC) data on
contributions to federal candidates and causes.
question is simple: what are the political preferences of Alphabet
employees, as revealed by their political giving histories, and how
have these preferences evolved over time?" GovPredict explained in
analysts and machines first had to identify the variants of employer
name that Alphabet employees used when filing election
contributions," GovPredict said. "The final list had 233 variants,
including 'Google Ventures,' 'Nest Labs,' 'Nest at Google,' 'Verily
(Google Life Sciences),' and the like." The researchers also had to
categorize as either Democrat or Republican "the 1,105 unique
committees to which Alphabet employees have contributed over the
past decade and a half."
majority of the party tags were supplied by the FEC; others were
categorized by hand. "Organizations that might not explicitly
identify with a political party but which ideologically are
synchronized were issued with a party label," they said. For
example, the League of Conservation Voters Action Fund was
categorized as a Democratic cause. In another example, "A
contribution in 2007 to Arlen Specter was categorized as a
contribution to a Republican, since he changed party affiliation in
findings were astounding, but not at all surprising to those of us
who have been paying attention to this issue: "Since 2004, Alphabet
employees have contributed a little over 90% of their political
dollars to Democratic candidates and causes," GovPredict discovered.
than $15 million in Alphabet employee political contributions went
to Democrat candidates and causes while a mere $1.6 million went to
Republicans between 20014 and 2017. The largest donation disparity
came in 2016 when 94 percent of contributions went to Democrats —
$5.8 million vs. $403,000, suggesting a strong reaction to President
Trump's election that prompted a flurry of donations.
has insisted over and over that they're not playing favorites.
Maggie Shiels from Google's corporate communications department
recently insisted to PJM that "Google does not manipulate results."
Charlie Martin recently explained how human
factors could skew an algorithm that determines what we see and
algorithm is nothing more than a procedure — a series of steps
that lead to a result. The word is mostly used with reference to
computer programs, but not necessarily — the way you learned to
do long division is an algorithm.
specific algorithms that are used come from the category of
"machine learning" or more broadly "artificial intelligence."
These phrases sound science-fictional and cool but the reality
is that all of these are doing something conceptually simple:
the programs get inputs, process them in various ways, and
present them to a person who says "you're getting warmer" or
"you're getting colder." This trains the program to get warmer
as often as possible.
potential weak spot here is the person in the loop. Imagine
you're Twitter and you have a machine learning algorithm you're
training to identify Nazis on Twitter. You put a person in the
loop who thinks Trump is a Nazi and anyone who says anything
favorable about Trump is a Nazi sympathizer. (They exist: I lost
a couple of friends when they called me a Nazi sympathizer for
just that reason.)
algorithm spots someone liking the tax cuts: the person says,
"he's a Nazi." The algorithm soberly notes that. It doesn't know
any better, it has no more understanding than an old-fashioned
tabulating machine understood why it put the A and B cards into
Brett Kavanagh? "He's a Nazi." In Congress with an (R) after
your name? "Oh yeah, definite Nazi."
quickly the algorithm will confidently identify any Republican,
any Trump fan, or any independent who says #MAGA, as a Nazi.