Mediaboss Marketing

The Democratic National Committee (DNC) has sent a letter to Facebook CEO Mark Zuckerberg containing yet another complaint about misinformation on the platform.

In the letter, obtained by Reuters, DNC CEO Seema Nanda wrote that we have significant remaining concerns about Facebook policies that allow the platform to be used to spread misinformation and undermine our democracy.

Democrats have been pressuring Facebook and its CEO, Zuckerberg, over its refusal to censor political ads in general, and Donald Trumps ads in particular. The DNC, Elizabeth Warren, Joe Biden and the left-wing media have all campaigned for Facebook to censor Trump ads.

In her letter, Nanda asks Facebook to dedicate additional capacity to enforce your terms of service against these types of malicious actors.

Current Facebook policy allows politicians to place demonstrable lies in front of voters via paid ads. This type of elite disinformation, from politicians many voters trust, is one of the most insidious and damaging forms of disinformation. Facebooks advanced analytics and targeting capabilities, furthermore, allow candidates to direct disinformation at the populations most susceptible to it, she continued.

However, the DNC CEO stopped short of calling for a Twitter-style blanket ban on political ads.

Banning political ads or severely inhibiting targeting capabilities on Facebook would not be in our partys best interest nor in the best interest of promoting voter participation, she wrote.

Nanda also claimed that the DNC has uncoveredat least nine foreign, inauthentic account networks targeting Americans with anti-Democratic false news content.

Its concerning that were able to uncover these terms-of-service-violating operations on a fairly regular basis, with a team far smaller than Facebooks, wrote Nanda.

The letter does not appear to have specified what methodology the DNC used to uncover these allegedly inauthentic accounts, why the DNC considered them inauthentic, or what country (or countries) they are from.

Are you an insider at Google, Reddit, Facebook, Twitter or any other tech company who wants to confidentially reveal wrongdoing or political bias at your company? Reach out to Allum Bokhari at his secure email addressallumbokhari@protonmail.com.

Allum Bokhari is the senior technology correspondent at Breitbart News.

Read the original:
DNC Demands More Censorship by Facebook of Misinformation - Breitbart

There's not really any dispute that the UK Liberal Democrats party sold voter data for 100,000 to the Remain campaign in 2016, though the Information Commissioner's Office tried to suppress that revelation until after the coming election; the Libdems say they did nothing wrong, but when Opendemocracy's Jim Cusick approached the party for a statement ahead of an article, he got no reply.

What happened next is...weird.

After Cusick's article went live, an aggrieved Libdem "senior official" wrote to Opendemocracy, demanding to know why their statement hadn't been included in the article. Cusick said it was because he'd never received a statement, but if they'd furnish one, he'd include it. But instead of a statement, Cusick got a legal threat from an expensive firm of solicitors, Goodman Derrick, demanding that the article be censored, either by removing "all derogatory and disparaging statements" (having read the article, I couldn't find any statements that qualified), or removal of the article altogether.

Given that the Libdems style themselves "the party of liberty," that is indeed weird.

But what happened next is weirder.

Opendemocracy asked the lawyers to provide a statement from the Libdems to include in their article, pointing out that they'd made three such requests without a response. In the absence of any statement from the Libdems (apart from the legal threat conveyed by their lawyers), Opendemocracy made a "surmise" about what the Libdems didn't like about their coverage and amended the article.

Then they heard from the lawyers again, stating that the Libdems had provided an "on the record" response to Cusick's article, on Nov 12, and they attached that email as proof.

Here's where the really weird stuff comes in.

Cusick didn't ask the Libdems for comment until Nov 13, which meant that the email the lawyers had attached as evidence had apparently been sent a full day before Opendemocracy wrote to the party seeking comment.

Opendemocracy wrote back to the lawyers, asking how this was possible.

When the lawyers did not reply, Opendemocracy wrote again, saying that they were about to publish a story about this and seeking comment. This time, someone from the Libdem press office called Opendemocracy and said a "mistake had been made" and said there was an investigation ongoing. So Opendemocracy generously gave the Libdems even more time to reply before publishing.

The party finally wrote back with a statement saying that "we have been made aware that the information openDemocracy subsequently received from the Liberal Democrats was incorrect. We have suspended a member of staff involved and are following due process."

But in addition to this, the Libdems' lawyers wrote back to Opendemocracy, repeating the threats over their coverage of the Libdems' data sale, and insisting that neither the lawyers nor the party had known about the fake email (Opendemocracy called it a "crude forgery"), despite the fact that Opendemocracy had painstakingly detailed their multiple attempts to solicit a comment from the party without a reply.

This is an embarrassment: as Opendemocracy points out, it doesn't rise to the level of open fraud committed by the Conservative Party and Boris Johnson, but the Tories don't style themselves "the party of liberty." Speaking as a former Libdem party member and campaigner (I'm a member of the Labour Party now), I don't believe the party should have flogged off voter data, but even moreso, I don't think that any party can be said to stand for "liberty" when its response to negative press coverage is to threaten to rain down expensive, punitive legal action from fancy lawyers.

First, why was the Lib Dem press office so desperate to discredit our story? In Jim Cusicks initial communications with them, he told them we had seen internal documents about the Lib Dems lucrative 2016 data sale. If, as they strongly maintain, the party had acted in accordance with the law at all times and had done nothing wrong, why did someone think it was important enough to repeatedly make false claims, including a faked document, via expensive lawyers?

What did our story reveal that prompted this level of duplicity?

Second, the replies from Goodman Derrick were issued on behalf of the party and of its leader, Jo Swinson. This assumes that senior figures were involved. Who sanctioned and signed off this aggressive legal pursuit, including the letter with the forged email? And how might Lib Dem supporters and donors feel about this appalling use of party funds?

Perhaps most importantly, though, what does this whole episode say about the so-called Liberal Democrats regard for fact-checking, accuracy and press freedom? We at openDemocracy are a small team. The distraction has cost us valuable staff time and legal bills, which could otherwise have been spent on doing actual journalism during the final weeks before the most important election in a generation.

What are Jo Swinsons Liberal Democrats so desperate to hide? [Mary Fitzgerald/Opendemocracy]

For many years, British Prime Minister Boris Johnson was a newspaper columnist for the Telegraph, where he espoused some of the most reactionary, disqualifying garbage ever published by a mainstream UK publication, a trend that continued after he began his political career. Business Insider has rounded up a "greatest hits" reel of Bojo's most disgusting []

For more than a decade, Britons have relied on Mysociety's Theyworkforyou to find out who represents them in Parliament (as well as the regional assemblies), find out what those people are doing and saying, and to get in touch with their lawmakers and their fellow constituents to hold them to account.

The UK is having an election in less than a month, and last night, Labour leader Jeremy Corbyn debated Tory archclown Boris Johnson for a televised debate in which Johnson had to defend his record as a lying, racist, philandering, cowardly failure of a human being, and of a leader to his party and the []

In the early days of the web, everyone wanted a .com domain for their site. As a result, all the good ones got snapped up. But .com no longer has the cachet it once did. In fact, many new businesses and individuals are opting for other top-level domain extensions. One of the most memorable is []

When the SNES launched back in the early 1990s, it changed gaming forever. One of the innovations was a gamepad with four action buttons something that has remained a constant on controllers ever since. The 8BitDo SN30 Bluetooth Gamepad brings that iconic design up to date, with Bluetooth connectivity and support for multiple platforms. []

After a long day at work, cooking a meal from scratch can seem like too much trouble. Unfortunately, the alternative is usually something unhealthy. Enter the Mellow Sous Vide Precision Cooker. This compact water bath uses cutting-edge technology to cook meat and veggies at the perfect temperature for exactly the right amount of time. It []

Original post:
Opendemocracy: the Libdems tried to censor our article about their sale of voter data, then used a forged email to intimidate us - Boing Boing

A small number of companies have begun developing new tools to measure employee engagement without requiring workers to fill out surveys or sit through focus groups. HR professionals and engagement experts are watching to see if these tools gain traction and lead to more effective cultural and retention strategies.

Two of these companiesNetherlands-based KeenCorp and San Franciscos Cultivateglean data from day-to-day internal communications. KeenCorp analyzes patterns in an organizations (anonymized) email traffic to gauge changes in the level of tension experienced by a team, department or entire organization. Meanwhile, Cultivate analyzes manager email (and other digital communications) to provide leadership coaching.

These companies are likely to pitch to a ready audience of employers, especially in the technology space. With IT unemployment hovering around 2 percent, corporate and HR leaders cant help but be nervous about hiring and retention. When competition for talent is fierce, companies are likely to add more and more sweeteners to each offer until they reel in the candidates they want. Then theres the matter of retaining those employees in the face of equally sweet counteroffers.

Thats why businesses utilize a lot of effort and money on keeping their workers engaged. Companies spend more than $720 million annually on engagement, according to the Harvard Business Review. Yet their efforts have managed to engage just 13 percent of the workforce.

Given the competitive advantage tech organizations enjoy when their teams are happy and productivenot to mention the money they save by keeping employees in placeengagement and retention are critical. But HR cant create and maintain an engagement strategy if it doesnt know the workforces mindset. So companies have to measure, and they measure primarily through surveys.

Today, many experts believe surveys dont provide the information employers need to understand their workforces attitudes. Traditional surveys have their place, they say, but more effective methods are needed. They see the answer, of course, in artificial intelligence (A.I.) and machine learning (ML).

One issue with surveys is they only capture a part of the information, and thats the part that the employee is willing to release, said KeenCorp co-founder Viktor Mirovic. When surveyed, respondents often hold back information, he explained, leaving unsaid data that has an effect similar to unheard data.

I could try to raise an issue that you may not be open to because you have a prejudice, Mirovic added. If tools dont account for whats left unsaid and unheard, he argued, they provide an incomplete picture.

As an analogy, Mirovic described studies of combat aircraft damaged in World War II. By identifying where the most harm occurred, designers thought they could build safer planes. However, the study relied on the wrong data, Mirovic said. Why? Because they only looked at the planes that came back. The aircraft that presumably suffered the most grievous damagethose that were shot downwerent included in the research.

None of this means traditional surveys surveys dont provide value. I think the traditional methods are still useful, said Alex Kracov, head of marketing for Lattice, a San Francisco-based workforce management platform that focuses on small and mid-market employers. Sometimes just the idea of starting to track engagement in the first place, just to get a baseline, is really useful and can be powerful.

For example, Lattice itself recently surveyed its 60 employees for the first time. It was really interesting to see all of the data available and how people were feeling about specific themes and questions, he said. Similarly, Kracov believes that newer methods such as pulse surveyswhich are brief studies conducted at regular intervalscan prove useful in monitoring employee satisfaction, productivity and overall attitude.

Whereas surveys require an employees active participation, the up-and-coming tools dont ask them to do anything more than their work. When KeenCorps technology analyzes a companys email traffic, its looking for changes in the patterns of word use and compositional style. Fluctuations in the products index signify changes in collective levels of tension. When a change is flagged, HR can investigate to determine why attitudes are in flux and then proceed accordingly, either solving a problem or learning a lesson.

When I ask you a question, you have to think about the answer, Mirovic said. Once you think about the answer, you start to include all kinds of other attributes. You know, youre my boss or youve just given me a raise or youre married to my sister. Those could all affect my response. What we try to do is go in as objectively as possible, without disturbing people as we observe them in their natural habitats.

Read more:

Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights

Machine learning has rapidly become one of those buzzwordsembraced by companies around the world. Even if they dont fully understandwhat it means, executives think that machine learning will magically transformtheir operations and generate massive profits. Thats good news fortechnologistsprovided they actually learn the technologys fundamentals, ofcourse.

Amazon wants to help with the learning aspect of things. At this years AWS re:Invent, the company is previewing the DeepComposer, a 32-key keyboard thats designed to train you in machine learning fundamentals via the power of music.

No, seriously. AWS DeepComposer is theworlds first musical keyboard powered by machine learning to enable developersof all skill levels to learn Generative AI while creating original musicoutputs, reads Amazonsultra-helpful FAQ on the matter. DeepComposer consists of a USB keyboardthat connects to the developers computer, and the DeepComposer service,accessed through the AWS Management Console.There are tutorials andtraining data included in the package.

Generative AI, the FAQcontinues, allows computers to learn the underlying pattern of a given problemand use this knowledge to generate new content from input (such as image,music, and text). In other words, youre going to play a really simple songlike Chopsticks,and this machine-learning platform will use that seed to build a four-hourWagner-style opera. Just kidding! Or are we?

Jokes aside, the ideathat a machine-learning platform can generate lots of data based on relativelylittle input is a powerful one. Of course, Amazon isnt totally altruistic inthis endeavor; by serving as a training channel for up-and-comingtechnologists, the company obviously hopes that more people will turn to it forall of their machine learning and A.I. needs in future years. Those interestedcan sign up for the preview on adedicated site.

This isnt the first time that Amazon has plunged into machine-learning training, either. Late last year, it introduced AWS DeepRacer, a model racecar designed to teach developers the principles of reinforcement learning. And in 2017, it rolled out AWS DeepLens camera, meant to introduce the technology world to Amazons take on computer vision and deep learning.

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

For those who master the fundamentals of machine learning, the jobs can prove quite lucrative. In September, theIEEE-USA Salary & Benefits Salarysuggested that engineers with machine-learning knowledge make an annual average of $185,000. Earlier this year, meanwhile, Indeed pegged theaverage machine learning engineer salary at $146,085, and its job growth between 2015 and 2018 at 344 percent.

If youre not interested in Amazonsversion of a machine-learning education, there are other channels. For example,OpenAI, the sorta-nonprofit foundation (yes, itsas odd as it sounds), hosts what it calls Gym, a toolkit fordeveloping and comparing reinforcement algorithms; it also has a set of modelsand tools, along with a very extensive tutorialin deepreinforcement learning.

Googlelikewise has acrash course,complete with 25 lessonsand 40+ exercises, thats a good introduction to machine learning concepts.Then theres Hacker Noon and its interesting breakdown ofmachine learning andartificial intelligence.

Onceyou have a firmer grasp on the core concepts, you can turn to BloombergsFoundations of Machine Learning,afree online coursethat teaches advanced concepts such asoptimization and kernel methods. A lotof math is involved.

Whateverlearning route you take, its clear that machine learning skills have anincredible value right now. Familiarizing yourself through thistechnologywhether via traditional lessons or a musical keyboardcan only helpyour career in tech.

Follow this link:

Amazon Wants to Teach You Machine Learning Through Music? - Dice Insights

The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions.

The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health.

Population health software, with or without machine learning, only produces suggestions. Getting a team to take action, particularly if that action is different, is one of the hardest things to do in healthcare. You will not succeed without executive support. Executives will not support you without significant incentive to do so.

Here's an easy surrogate for whether there is enough of that incentive: whether those executives jobs are in jeopardy if too many people go to the hospital. If not, the likelihood that an investment will lead to measurable improvement is minimal.

If youve been ordered to "do" population health, your best bet is to install a low cost risk score or have your team write a query to identify the oldest sickest people with the most readmissions. Either will return the same results more or less and your team of care managers are used to ignoring said results without rocking the boat. If there is sufficient incentive, read on.

Henry Ford is credited with saying, "If I asked people what they wanted, they would have said faster horses." Its human nature to try to apply a new technology in an old way.

Economists have named this the IT Productivity Paradox and have studied the cost of applying new technical capabilities in old ways. There are signs that healthcare organizations are unknowingly walking this plank.

For decades, risk scores were designed to identify the costliest patients with little consideration of the types of costs, the diseases they suffer, whether or not those costs are preventable, etc.

As a result, according to a systematic review of 30 risk stratification algorithms appearing in the Journal of the American Medical Association, "most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly." A recently published study in Science also showed that prioritizing based on cost discriminates against people of color. Applying more data and better math to solve the problem in the old way is an expensive way to propagate existing shortcomings.

The opportunity now made possible is the ability to match individuals to interventions. Patients with serious mental illness that are most likely to have an inpatient psychiatric admission are very different than those with serious illnesses that might benefit from home-based palliative care. Clinicians wouldnt treat them the same, neither should our approach to prioritization.

However, you will need to design for this and clinical teams should be prepared for the repercussions. Patients identified with rising risk (as opposed to peak utilization) will not seem as sick.

Clinical teams trained to triage may feel like theyre not doing their jobs if the patients arent as obviously acute. Its important to discuss these repercussions and prepare in advance of the introduction of new technology.

Using technology to send more of the right people into a program that doesnt have an impact only adds to the cost of an already failing program. Surprisingly, very few programs have ever measured the impact of their interventions.

Those that have, often rely on measuring patients before and after they enter into care management programs which is misleading and biased on many levels.

If you are not confident that the existing program makes a difference, invest in measuring and improving the existing programs performance before investing additional resources. A good read on the pros and cons of different approaches to measuring impact is here.

Starting with a program of measurement can create a culture of measurement, improvement, and accountability - a great foundation for a pop health effort. Involving the clinical team in the definition of measures that matter will go a long way.

Another important consideration is whether your intervention is costly to deliver. The more costly it is to steer resources toward the wrong people, the more likely your program is to benefit from smarter prioritization.

For both reasons above if your program is entirely telephonic and targets older people with chronic complex diseases, you may want to invest in program design and measurement before investing in stratification technology.

Youre in great shape, and your odds of success are exponentially higher. Youre also better informed, as you and the team shift focus to decisions such as whether to build versus partner, what unique data you collect that can be used to your advantage and how youll measure algorithm and program performance.

Leonard DAvolio, PhD is an assistant professor at Harvard Medical School and Brigham and Womens Hospital, and the CEO and founder of Cyft. He shares his work on LinkedIn and Twitter.

See the original post:

3 questions to ask before investing in machine learning for pop health - Healthcare IT News