No doubt these machines were each “tested and certified.”
The
electronic votes said he lost in a statistically impossible
landslide, but the paper ballots said he won
Vote totals in a
Northampton County judge’s race showed one candidate, Abe Kassis, a
Democrat, had just 164 votes out of 55,000 ballots across more than
100 precincts. Some machines reported zero votes for him. In a
county with the ability to vote for a straight-party ticket, one
candidate’s zero votes was a near statistical impossibility.
Something had gone quite wrong.
… When
officials counted the paper backup ballots generated by the same
machines, they realized Kassis had narrowly won.
It
is still unknown what caused the problem since the machines "are
locked away for 20 days after an election according to state law."
However, suspicions
include a
bug in the software, as well as a fundamentally flawed design.
You
don’t ask for evidence when you believe you “have to do
something!”
Schools
Spy on Kids to Prevent Shootings, But There's No Evidence It Works
Your Holiday reading?
Privacy
Papers 2019
THE WINNERS OF THE 2019 PRIVACY PAPERS FOR
POLICYMAKERS (PPPM) AWARD ARE:
Some companies are just massive fines waiting to
happen.
Getting
Cookie Consent Right
One could be forgiven for thinking that knowing
how to comply with a legal obligation that has been in place for
nearly a decade would be clear cut. However, widespread practice
tells us that this is far from the truth.
Too simple? What would it take for everyone to
agree?
DARPA
SCIENTIST: ENGINEERS MUST STOP MAKING AUTONOMOUS WEAPONS
If
we really want to prevent the rise of autonomous weapons — killer
robots that can pull the trigger without needing a human’s approval
— then engineers will actually need to stop
working towards them.
So
argues Christoffer Heckman, a University of Colorado
Boulder computer scientist who’s funded by DARPA, the Pentagon’s
research division, in an essay
in The
Conversation
It may sound like an obvious solution, but Heckman points out that
it’s sometimes hard for researchers to predict how their work might
get used or abused in the future.
The
‘Double Secret Probation’ debate.
Justices
debate allowing state law to be “hidden behind a pay wall”
Ars
Technica –
“The
courts have long held that laws
can’t be copyrighted.
But if the state mixes the text of the law together with supporting
information, things get trickier. In Monday oral arguments, the US
Supreme Court wrestled with the copyright status of Georgia’s
official legal code, which includes annotations written by
LexisNexis. The defendant in the case is Public.Resource.Org (PRO),
a non-profit organization that publishes public-domain legal
materials. The group obtained Georgia’s official version of state
law, known as the Official Code of Georgia Annotated, and published
the code on its website. The state of Georgia sued, arguing that
while the law itself is in the public domain, the accompanying
annotations are copyrighted works that can’t be published by anyone
except LexisNexis. Georgia won at the trial court level, but PRO won
at the appeals court level. On Monday, the case reached
the US Supreme Court.
“Why would we allow the official law to be hidden behind a pay
wall?” asked Justice Neil Gorsuch. Georgia’s lawyer countered
that the law wasn’t hidden behind a paywall—at least not the
legally binding parts. LexisNexis
offers a free
version
of
Georgia’s code, sans
annotations,
on its website. But
that version isn’t the official code.
LexisNexis’ terms of service explicitly warns users that it might
be inaccurate. The company also prohibits users from scraping the
site’s content. If you want to own the latest official version of
the state code, you have to pay LexisNexis hundreds of dollars. And
if you want to publish your own copy of Georgia’s official code,
you’re out of luck…”
Tools
for Big Data?
Netflix:
Our Metaflow Python library for faster data science is now open
source
… The
video-streaming giant uses machine learning across all aspects of its
business, from screenplay analysis, to optimizing production
schedules, predicting churn, pricing, translation, and optimizing its
giant content distribution network.
According
to Netflix software engineers,
Metaflow was built to help boost the productivity of its data
scientists who like to express business logic through Python code but
don't want to spend too much time thinking about engineering issues,
such as object hierarchies, packaging issues, or dealing with obscure
APIs unrelated to their work.
… Netflix
offers this nutshell description of its Python library on the new
metaflow.org
website:
"Metaflow helps you design your workflow, run it at scale, and
deploy it to production. It versions and tracks all your experiments
and data automatically. It allows you to inspect results easily in
notebooks."
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