Data Science newsletter – April 5, 2021

Iowa lawmakers will not be banning tenure at the state’s three public universities this year after a bill to do so failed to advance in the Legislature.

The Iowa House did not vote to pass the measure, House File 496, ahead of a key deadline this week, and the bill never made it through a committee in the Senate. The legislation would have needed to pass out of either the House or the Senate and through a committee in the other chamber by the end of this week in order to survive this year.

House Speaker Pat Grassley, R-New Hartford, said Republicans are still interested in changing the tenure system.

Conversations around AI and ethics may have started as a preoccupation of activists and academics, but now — prompted by the increasing frequency of headlines of biased algorithms, black box models, and privacy violations — boards, C-suites, and data and AI leaders have realized it’s an issue for which they need a strategic approach.

A solution is hiding in plain sight. Other industries have already found ways to deal with complex ethical quandaries quickly, effectively, and in a way that can be easily replicated. Instead of trying to reinvent this process, companies need to adopt and customize one of health care’s greatest inventions: the Institutional Review Board, or IRB.

What happens to Cassandras when their warnings become reality? If you are epidemiologist Larry Brilliant, you work to mitigate a situation that would not have been so terrible if people had listened to you in the first place. Pre-Covid Brilliant, along with many of his peers, had been ringing the alarm on pandemics in op-eds, a much-viewed TED talk, and a tragically prophetic horror movie he advised on called Contagion. In the last year, Brilliant—best known for his work in helping to eradicate smallpox—has been active in helping people understand Covid-19, as founder and CEO of Pandefense Advisory.

Now, along with noted epidemiologist Ian Lipkin and Pandefense Advisory colleagues Lisa Danzig and Karen Pak Oppenheimer, he has proposed a plan to help us avoid an unnecessarily lengthy recovery. Basically, Brilliant and his coauthors are instructing us to discard the panacea of herd immunity and gird ourselves for localized combat against a virus that produces ever more infectious variants. Ultimately, Brilliant envisions a framework that will not only get us to normalish but also position us to fend off pandemics to come.

The hardware or software that operates medical devices can be biased. A biased device is one that operates in a manner that disadvantages certain demographic groups and influences health inequity. As one measure of fairness, reducing bias is related to increasing fairness in the operation of a medical device. Initiatives to promote fairness are rapidly growing in a range of technical disciplines, but this growth is not rapid enough for medical engineering. Although computer science companies terminate lucrative but biased facial recognition systems, biased medical devices continue to be sold as commercial products. It is important to address bias in medical devices now. This can be achieved by studying where and how bias arises, and understanding these can inform mitigation strategies.

Bias in medical devices can be divided into three broad forms (see the figure). A medical device can exhibit physical bias, where physical principles are biased against certain demographics. Once data are collected, computational bias, which pertains to the distribution, processing, and computation of data that are used to operate a device, must be considered. Subsequent implementation in clinical settings can lead to interpretation bias, where clinical staff or other users may interpret device outputs differently based on demographics.

When Jessica Flake started her Ph.D. at the University of Connecticut, she hadn’t yet figured out that published papers were the currency of academia. Flake, who describes herself as growing up in poverty and was the first in her family to attend college, let alone pursue a Ph.D., found herself navigating an increasingly foreign landscape as she pursued her academic career. “You just don’t know how it works,” says Flake, who is now an assistant professor of psychology at McGill University, and “it gets worse the higher up you go.”

A new study quantifies how underrepresented people like Flake are in academia, at least in the United States, finding that tenure-track faculty come from homes wealthier than the average population and are 25 times more likely than the general population to have a parent with a Ph.D. Compared with the wider population of their Ph.D.-holding peers, tenure-track faculty are also nearly twice as likely to have Ph.D.-holding parents. That’s based on a survey of more than 7000 U.S.-based tenure-track faculty across eight STEM, social science, and humanities disciplines, reported last week in a preprint posted to SocArXiv. The findings suggest that academia is still accessible largely to people from privileged—and academic—families, highlighting a barrier that intersects with race to limit the diversity of the academy, says lead author Allison Morgan, a University of Colorado (CU), Boulder, Ph.D. student who researches diversity in science. Because Black and Hispanic scholars, among other groups, are underrepresented among current Ph.D. holders, generational effects could impede efforts to diversify academia for many years to come, Morgan adds.

Research is increasingly showing how drought, heat, and wildfire influence each other. Ongoing collaborations provide templates for how best to study these phenomena and plan for their future impacts.

Sixteen years ago, many of us held a printout of directions in one hand and the steering wheel in the other to get around— without information about the traffic along your route or details about when your favorite restaurant was open. Since then, we’ve been pushing the boundaries of what a map can do, propelled by the latest machine learning. This year, we’re on track to bring over 100 AI-powered improvements to Google Maps so you can get the most accurate, up-to-date information about the world, exactly when you need it. Here’s a snapshot of how we’re using AI to make Maps work better for you with a number of updates coming this year.

John Deere is leveraging Intel’s artificial intelligence (AI) technology to help solve a costly, age-old problem in the manufacturing welding process. Deere is piloting a solution that uses computer vision to automatically spot common defects in the automated welding process in its manufacturing facilities.

“Welding is a complicated process,” said Andy Benko, quality director, John Deere Construction & Forestry Division. “This AI solution has the potential to help us produce our high-quality machines more efficiently than before. The introduction of new technology into manufacturing is opening up new opportunities and changing the way we think about some processes that haven’t changed in years.”

At 52 factories around the world, John Deere uses the Gas Metal Arc Welding (GMAW) process to weld mild- to high-strength steel to create machines and products. In these factories, hundreds of robotic arms consume millions of weld wire pounds annually.

Refraction AI is a robot delivery startup based out of Ann Arbor, Michigan, the United States. It offers easy and efficient food delivery from restaurants to consumers. This delivery is made using a self-driving robot manufactured by Refraction AI (REV-1), working on machine learning algorithms. The robot so offered is claimed to be lightweight, low cost, and autonomous, thereby making food delivery hassle-free. In the recent Seed funding rounds, the organization raised $ 4.2 million, which takes the total funding to a little more than $10 million. The capital raised would be utilized in the direction of geographic expansion, product development, and consumer acquisition.

The organization was started by the University of Michigan and started delivering food to the consumers in the year 2019. The minds behind Refraction AI are Matthew Johnson-Roberson and Ram Vasudevan. The company’s primary aim is to bring in a small fleet of delivery robots that would lessen the employees’ workload and ensure contact-less and low-cost deliveries, which would be beneficial for both the restaurants and the consumers. Refraction AI innovatively uses machine learning to deliver goods to people. While doing so, they also ensure that the business costs are reduced, the roads are less congested, and the carbon emissions are minimal (sustainability).

[Linda] Findley said she just isn’t sure that every case reported as the coronavirus really is the virus: “Everything seems to be coronavirus. I mean, it’s just … no matter what somebody has, it’s coronavirus. I don’t know whether it is or isn’t.”

Fort Scott is one of nearly 140 rural communities that have lost a hospital in the past decade. Mercy Hospital Fort Scott closed in December 2018.

Even though critically ill patients now must travel to hospitals farther away, Fort Scott residents haven’t seen that as a pandemic-related problem. Rather, not having a hospital doesn’t really come up when people here talk about covid.

The Leveraging Integrated Networks in Communities (LINC) to Address Social Needs Act will award grants to states to develop or enhance collaboration between health care organizations and social service sectors. The legislation, co-sponsored by Senators Dan Sullivan (R-AK) and Chris Murphy (D-CT), authorizes $150 million in funding for states that would be awarded by the Secretary of Health and Human Services to develop or enhance cross-sector data sharing capabilities and “
closed loop referral systems,” which leverage technology-based community service inventories to support identified
health-related social needs of individuals. The objective is to address longstanding challenges in communication on referrals, capacity management, and outcome tracking across sectors. The funding approach would allow states to pursue a variety of approaches to fit local circumstances and foster innovation.

St. Lawrence University faculty members have voted in favor of creating bachelor’s degree programs in data science and finance, two cutting-edge fields where graduates are in high demand.

Both major proposals are with the New York State Education Department for final approval. If approved to be added to the University’s curriculum, St. Lawrence would be among a very small number of liberal arts colleges to offer either program.

After more than 19 years leading the institute, Jacks is handing the reins to Matthew Vander Heiden, an MIT biology professor and cancer cell metabolism researcher. The longtime director isn’t going far: MIT President L. Rafael Reif said in October that Jacks will return “full time to the excitement of the lab.” … Vander Heiden plans on steering the Koch Institute toward the way of the future, with a focus on machine learning and AI.

“A.I. Nation” is co-hosted by Ed Felten, the Robert E. Kahn Professor of Computer Science and Public Affairs and founding director of Princeton’s Center for Information Technology Policy, and WHYY reporter Malcolm Burnley. Over the course of five episodes, the pair will investigate how artificial intelligence is affecting our lives right now, and the impact that technologies like machine learning, automation and predictive analytics will have on our future.

In a rapidly changing digital landscape, the School of Information Studies is challenged to ensure that its students and faculty are studying at the leading edge of their field. From human-centered design to cloud computing, machine learning to artificial intelligence and beyond, students and faculty are asking the questions that drive the future of business, technology and society.

It’s with this spirit of innovation that the iSchool announces the launch of two new undergraduate programs: innovation, society and technology and applied data analytics. These two new majors will complement the current information management and technology program and will provide further opportunity for inquiry and exploration.

Online April 21-23. [free, registration required]

Over 2 million users visit Shopify’s Help Center every month to find help solving a problem. They come to the Help Center with different motives: learn how to set up their store, find tips on how to market, or get advice on troubleshooting a technical issue. Our search product helps users narrow down what they’re looking for by surfacing what’s most relevant for them. Algorithms empower search products to surface the most suitable results, but how do you know if they’re succeeding at this mission?

Below, we’ll share the three-step framework we built for evaluating new search algorithms. From collecting data using Kafka and annotation, to conducting offline and online A/B tests, we’ll share how we measure the effectiveness of a search algorithm.

Auditing CSS is not a common task in a developer’s everyday life, but sometimes you just have to do it. Maybe it’s part of a performance review to identify critical CSS and reduce unused selectors. Perhaps is part of effort to improve accessibility where all the colors used in the codebase evaluated for contrast. It might even be to enforce consistency!

Whatever the case and whenever that moment arrives, I usually reach for some of the tools I‘ll cover in the article. But before that, let’s see what it even means to “audit” CSS in the first place.