Data Science newsletter – June 1, 2020

GROUP CURATION: N/A

As infrastructure providers, publishers, educators, it’s important for us to consider our response, so far, and going forward, calibrating our expectations according to the scientific process without assumptions that we could or have created in silo about how best to support scientists and data sharing right now.

Ethical behavior takes time

We are living in an unprecedented time. Unlike previous pandemics such as the Spanish flu and even the more recent onset of the AIDS crisis, we now live in the era of “big data” and we have the ability to rapidly disseminate results. We have supportive infrastructure in place to educate researchers on data sharing and we have sturdy technical frameworks for disseminating and hosting these data. And while speed and quantity can be seen as wins, there is a balance with quality. Importantly, there is a real need to understand pitfalls ahead if we solely focus on rushing or false advertising, misaligned with bringing value to scientific research publishing.

The University of Washington today announced the establishment of the Center for Research and Education on Accessible Technology and Experiences (CREATE). Fueled by a $2.5 million inaugural investment from Microsoft, UW CREATE is led by an interdisciplinary team whose mission is to make technology accessible and to make the world accessible through technology.

“We are proud to partner with the UW on their journey to build the CREATE center,” said Brad Smith, president of Microsoft. “This is the next step in a longstanding journey to empower people with disabilities with accessibility and technology advancements. UW has truly embedded accessibility as part of their culture and we’re proud to support their next step to drive thought leadership on accessibility to empower people with disabilities.”

A nationwide collaboration of clinicians, informaticians and other biomedical researchers aims to turn data from hundreds of thousands of medical records from coronavirus patients into effective treatments and predictive analytical tools that could help lessen or end the global pandemic.

Through the National COVID Cohort Collaborative, about 60 clinical institutions affiliated with the National Institutes of Health-supported Clinical and Translational Science Awards Program are invited to partner with U.S. Department of Health & Human Services agencies and clinical organizations. Together, Collaborative members will support the analysis of electronic health records on a new, secure database.

The National COVID Cohort Collaborative is supported as part of a $25 million NIH award to the National Center for Data to Health, which is coordinating the collaborative’s efforts and is based at Oregon Health & Science University’s Oregon Clinical and Translational Research Institute. NIH’s National Center for Advancing Translational Sciences, also known as NCATS, is providing overall stewardship of the Collaborative.

UW-Madison has named astronomer Eric Wilcots to lead the university’s largest academic unit, marking the first time an African American will serve as dean of the College of Letters and Science.

Wilcots, 55, will oversee a $356 million budget, about 20,000 students, more than 800 faculty members, 37 departments, and multiple professional schools as well as the recently launched School of Computer, Data and Information Sciences. He has managed those responsibilities in an interim role since August.

In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the “Concerned Home Office Associates.” (Walmart’s headquarters in Bentonville, Arkansas, is often referred to as the Home Office.) While it’s not unusual for journalists to receive anonymous tips, they don’t usually come with their own slickly produced videos.

The employees said they were “past their breaking point” with Everseen, a small artificial intelligence firm based in Cork, Ireland, whose technology Walmart began using in 2017. Walmart uses Everseen in thousands of stores to prevent shoplifting at registers and self-checkout kiosks. But the workers claimed it misidentified innocuous behavior as theft, and often failed to stop actual instances of stealing.

They told WIRED they were dismayed that their employer—one of the largest retailers in the world—was relying on AI they believed was flawed. One worker said that the technology was sometimes even referred to internally as “NeverSeen” because of its frequent mistakes. WIRED granted the employees anonymity because they are not authorized to speak to the press.

More than 4,000 people have died in a country of ten million. For seven of the last 14 days, Sweden has had the highest number of deaths per capita in the world. “Sweden hasn’t changed very much at all,” says Paul Franks, an epidemiologist at Lund University. “But because things have changed in other countries, you’ve noticed the change in the relative death rates.” The comparison is particularly stark when compared to Sweden’s neighbours, which have similar cultural practices and healthcare systems – it has almost four times as many deaths as Norway, Finland and Denmark combined.

So what went wrong? How did Sweden go from a poster child for the lockdown skeptics to one of the worst-hit countries in Europe?

Now that there are glimmers of hope for a coronavirus vaccine, governments, NGOs and others are hashing out plans for how vaccines could be distributed once they are available — and deciding who will get them first.

Why it matters: Potential game-changer vaccines will be sought after by everyone from global powers to local providers. After securing supplies, part of America’s plan is to tap into its military know-how to distribute those COVID-19 vaccines.

The U.S. has suffered from devastating wildfires over the last few years as global temperatures rise and weather patterns change, making the otherwise natural phenomenon especially unpredictable and severe. To help out, Stanford researchers have found a way to track and predict dry, at-risk areas using machine learning and satellite imagery.

As protesters take to the streets, they’ll be watched by law enforcement agencies that have trialed or are currently deploying a variety of surveillance tools. The Minneapolis Police Department has used an array of technologies in the past —including Clearview AI, which has scraped billions of photos from social media to power its facial recognition tool. Nearby police departments, as well as the Hennepin County Sheriff’s Office and the Minnesota Fusion Center — which maintain jurisdictions that overlay Minneapolis — have also used Clearview.

“At a high level, these surveillance technologies should not be used on protesters,” Neema Singh Guliani, a senior legislative counsel for the ACLU, told BuzzFeed News. “The idea that you have groups of people that are raising legitimate concerns and now that could be subject to face recognition or surveillance, simply because they choose to protest, amplifies the overall concerns with law enforcement having this technology to begin with.”

What are we finding so far? The introduction of access to collective bargaining drives a modest decline in policy employment and increase in compensation with no meaningful impacts on total crime, violent crime, property crime or officers killed in the line of duty. [thread]

Facing the biggest economic crisis in the EU’s history, the European Commission’s president, Ursula von der Leyen, pulled out all the stops this week, unveiling an unprecedented plan to boost the union’s post-coronavirus recovery.

The plan: The EU would go to international capital markets to raise 750 billion euros ($830 billion). 500 billion of that would be given to member states as grants to fund economic recovery over the next seven years; the remainder would be issued as loans to be paid back to Brussels. The EU would pay back its bondholders for the full 750 billion plus interest by 2058, in part by raising new EU-wide taxes on tech companies and emissions.

Never before has the EU raised debt of this magnitude. But without it, von der Leyen warned, the EU would risk splintering into “haves and have-nots” – that is, countries that have the money to rescue themselves and those that don’t.

Retailers are now thinking about all levels of the shopping experiences — from the parking lot to the cash register. Normal actions we once took for granted are being upended. We took a look at some of the ways stores will change in the coming weeks and months. Before, retailers promised to make small incremental changes to make their brick and mortar experiences more up to date. Now, they are being forced to act much more quickly.

Here’s a look at some of the biggest problems and changes on the horizon for retailers while they begin reopening. Let’s take a walk through the store.

Inductiv developed technology that uses artificial intelligence to automate the task of identifying and correcting errors in data. Having clean data is important for machine learning, a popular and powerful type of AI that helps software improve with less human intervention.

The work falls under the category of data science, a key element of Apple’s broader machine-learning strategy. In 2018, the company brought on several engineers from Silicon Valley Data Science, a consulting firm that focuses on this field.

John Giannandrea, the Apple executive in charge of Siri and machine learning, has been upgrading the underlying technology that goes into the Siri digital assistant and other AI-powered products from the company.

Timothy Sheahan, a virologist studying COVID-19, wishes he could keep pace with the growing torrent of new scientific papers related to the pandemic. But there have just been too many—more than 5000 papers a week. “I’m not keeping up,” says Sheahan, who works at the University of North Carolina, Chapel Hill. “It’s impossible.”

A loose-knit army of data scientists and software developers is pressing hard to change that. They are creating digital collections of papers and building search tools powered by artificial intelligence (AI) that could help researchers quickly find the information they seek. The urgency is growing: The COVID-19 literature has grown to more than 31,000 papers since January and by one estimate is on pace to hit more than 52,000 by mid-June—among the biggest explosions of scientific literature ever.

The volume of information “is like what you would get in a medical conference that used to happen yearly. Now, that’s happening daily,” says Sherry Chou, a neurologist at the University of Pittsburgh Medical Center who is studying COVID-19’s neurologic effects.

Robust testing is key to safely reopening the economy. But a new model shows that if testing is not paired with “smart containment,” it could backfire.

Online August 23-27. “The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) today announced that registration is open for KDD 2020, the premier interdisciplinary data-science conference. For 26 years, the KDD conference has brought together an international community of leading researchers and practitioners in data science, machine learning, big data, and artificial intelligence. Given the rapidly changing landscape of the global pandemic, KDD 2020, which will take place Aug. 23-27, 2020, will now offer its signature programming in an entirely virtual format.”

Online June 12, starting at 10 a.m. EDT. ”
The National Academies’ Standing Committee on Advancing Science Communication invites you to join a webinar focusing on the role of science communication and engagement to meet the needs of communities disproportionately affected by COVID-19, including low-income African-American, Native American, and diverse, multi-lingual communities.” [registration required]

Kaggle is a data science community of nearly 5 million users. In September of 2019, we found ourselves under a sudden siege of spam traffic that threatened to overwhelm visitors to our site. We had to come up with an effective solution, fast. Using AutoML Natural Language on Google Cloud, Kaggle was able to train, test, and deploy a spam detection model to production in just eight days. In this post, we’ll detail our success story about using machine learning to rapidly solve an urgent business dilemma.

Every map is infused with its maker’s decisions, which ultimately present a pattern, story or argument. Sometimes those choices of design, labeling, data selection, and data slicing show up as obvious biases, as in the case of Donald Trump’s infamously augmented 2019 map of Hurricane Dorian. More often, though, this inherent “truthiness” flies under the radar of a map’s tidy, matter-of-fact visual presentation, as in the many maps and models being made now of semi-reliable Covid-19 case data.

So while it’s relatively easy to make a map in an age of abundant data and digital tools, it isn’t always easy to read them. How can you tell what’s real, and what’s a distortion?