Thursday, December 05, 2019


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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