Dead-Simple Algorithm Reveals the True Toll of Voter ID Laws

["Dave Farber" <dave () farber net>]

---------- Forwarded message ---------
From: Dewayne Hendricks <dewayne () warpspeed com>
Date: Thu, Jan 4, 2018 at 2:14 PM
Subject: [Dewayne-Net] A Dead-Simple Algorithm Reveals the True Toll of
Voter ID Laws
To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>


[Note:  This item comes from friend David Rosenthal.  DLH]

A Dead-Simple Algorithm Reveals the True Toll of Voter ID Laws
By ISSIE LAPOWSKY
Jan 4 2018
<https://www.wired.com/story/voter-id-law-algorithm/>

After announcing the closure of his short-lived commission to end voter
fraud, President Trump made it clear Thursday that he wants more states to
require identification at the ballot box to prevent what he believes is
rampant—but still unproven—election rigging.

Ever since the Supreme Court struck down a key part of the Voting Rights
Act in 2013, laws requiring voters to show identification when they vote
have speckled the nation, popping up in states from Rhode Island to
Arizona. Almost as quickly, voting rights advocates have taken states like
Texas and Alabama to court, arguing that these laws intentionally
discriminate against minority voters. Just last summer, a federal judge
tossed out Texas’s voter ID law, in a case that’s now being revisited by an
appeals court. But proving exactly how discriminatory these laws are
requires far more complexity than it might seem.

Sure, there are endless anecdotes of well-meaning, well-prepared citizens
being turned away on election day, but anecdotes are not data. There are
ample surveys asking voters whether these laws came between them and the
ballot box, but people can always misrepresent themselves on surveys, and
courts tend to dismiss them, anyway. Seven states include Social Security
numbers in voter files and driver's license records, but across the rest of
the country, determining whether a single individual voter is also listed
in any number of identification databases has become a complex and
nettlesome problem for voting access advocates and statistics researchers
alike.

Recently, however, researchers at Tufts University and Harvard University
demonstrated that it's possible to match individuals across government
databases with nearly perfect accuracy, using just a few basic identifiers
like a person’s name, date of birth, and address. They developed the
algorithm while working as expert witnesses in the Department of Justice's
case against Texas. Now, in a newly published paper, researchers Stephen
Ansolabehere of Harvard and Eitan Hersh of Tufts have explained the
underlying methodology. Their goal, according to Hersh, is to create a
system courts can easily understand, which can not only be used in future
voter ID law cases, but can also help dispel some myths about who those
laws do and don’t hurt.

“The more we can agree on methods that are easy to explain, the better off
we are,” says Hersh.

A Better Model

If all data were clean and complete, it wouldn’t be so hard to figure out
if a voter named, say, John Smith, was the same John Smith listed in the
federal driver’s license database. According to Hersh and Ansolabehere’s
research, only 1 in 2.7 billion individuals have the same zip code, gender,
date of birth, and last name, making those four details combined a fairly
spot-on indicator of a person’s identity. But often, government records
contain typos, incomplete fields, nicknames, or outdated addresses. To link
databases, researchers often need to make do with less information.

They also need to be able to show their work in a way that lawyers and
judges can understand. So many algorithms that purport to match people
across databases run up against the so-called black box problem. They may
be able to make statistically sound decisions, but they can't easily
explain how they made them. In a recent Supreme Court hearing over partisan
gerrymandering in Wisconsin, Chief Justice John Roberts dismissed
research-backed methods to measure gerrymandering as "sociological
gobbledygook." Hersh and Ansolabehere wanted to develop a tool that could
be easily understood.

[snip]

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