Data sharing is the crux of the issue. Fitness app companies are often incentivized to share your valuable real-time health data with third parties, whether they are advertisers, law firms, or social networks like Facebook that profit from your sensitive information. If they were fully transparent about how your data was shared or how to adjust your privacy settings, users might be less likely to trust the apps. That’s why, to date, the fitness and health app industry has been dogged by scandals.
There are many valid reasons for an app to share data. It can lead to better service that the user wants. It can also be required by law for police investigations. But app makers don’t always treat the privacy of your sensitive information as a top priority.
Health Affairs; Ziad Obermeyer, Brian Powers, Christine Vogeli, Sendhil Mullainathan
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Our recent paper in Science showed that an algorithm widely used for population health management has significant racial bias. The scale of impact is large, affecting health care decisions for at least tens of millions of patients every year. The magnitude of bias is also large: removing bias from the algorithm would more than double the number of Black patients eligible for a program that gives extra medical help to the neediest patients.
Media coverage of our findings (in the Wall Street Journal, Washington Post, LA Times, Wired, and other outlets) has focused on the scale and impact of bias we uncovered. This doesn’t apply to just one algorithm or one manufacturer, but the general approach to predicting risk used by nearly all health systems and insurance companies.
A New Initiative to Address Bias in Health Care Algorithms
What’s next for the health care system? We need algorithms to help with the critical work of population health management: scaled decisions for millions of patients are not a suitable task for human doctors or policy makers alone. But how do we ensure that these algorithms are doing their job fairly?
[Cesar] de la Fuente and his team use computer algorithms to design and refine new drugs, sifting through chemical building blocks for combinations that are predicted to penetrate bacterial defenses.
He described one such success in a study last year while at Massachusetts Institute of Technology: a twisting, branching molecule that disrupted the membranes of bacteria called Pseudomonas in infected mice, yet left the animals’ own cells intact.
Artificial intelligence, machine learning and deep learning (machine learning on steroids) can be used for purposes that enhance market competition or are anti-competitive, wrote Antonio Capobianco of the Organization for Economic Cooperation and Development in a January paper titled “Digital Cartels and Algorithms.”
Positive examples of commercial algorithms are legion: supply-chain optimization; targeted ads; recommendations; product customization; dynamic pricing; price differentiation, and fraud prevention. Anti-competitive algorithms include bias in favor of incumbents’ products and collusion with competitors.
Algorithms can be used for collusion in setting prices for essentially identical goods, such as gasoline at the pump or airline tickets.
Perhaps most importantly, we are not sure how AI algorithms will interact with each other in the jungles of Wall Street. In capital markets, stock prices depend heavily on the decisions of other participants in the market. If most of the participants are AI-driven, and they adopt broadly similar machine-learning strategies, they might create echo effects where they all pile into (or out of) a stock at a moment’s notice. Flash crashes might become more frequent as a result.
This is particularly troubling given the rise of simple yet devastatingly effective adversarial strategies that attempt to fool AI algorithms into behaving in unexpected ways. For example, one study found that affixing a few small black and white stickers onto a stop sign tricked an image-recognition algorithm into never recognizing it.
Zald will join Rutgers in May 2020. His responsibilities will include organizing the magnetic resonance imaging (MRI) human brain imaging core facility to support research of faculty and trainees at Rutgers, Rutgers Biomedical and Health Sciences, the Brain Health Institute and the Center for Computational Cognitive Neuropsychiatry.
The new research center, opening in fall 2020, will be housed in the Staged Research Building on Busch campus in Piscataway and include a research-dedicated 3T Siemens MAGNETOM Prisma MRI scanner. The state-of-the-art scanner assesses changes in blood flow, oxygen consumption and glucose use in the brain to noninvasively measure structure and activity of the human brain.
The Pudding; Russell Goldenberg, Kishan Sheth, Caitlyn Ralph & Jan Diehm
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Today, lol is prodigious. It can be found once every 54 comments on Reddit. But has that always been the case? We went back 10 years (for reference: Obama’s first year in office) to find out. Here is the rise of lol in one chart.
Since the 2017 NFL season, Next-Gen Stats, which is powered by Amazon Web Services, provides a look into the game that fans of football past would never think about.
To calculate this data, radio frequency identification devices — or RFIDs —attached to the players shoulder pads and the ball capture data from points across the stadium to “track the players and ball movement down to the inch.”
This data is eventually combined with traditional data such as box score and play-by-play data to “capture 100’s of metrics never captured before.”
Students interested in data analytics can now take a new Wharton class, “Data Science for Finance,” which will prepare students for future careers in a range of fields, from investment banking to consulting.
Wharton finance professor Michael Roberts will teach the class, which will be offered for the first time in fall 2020, and will use big data analysis to answer finance questions. The course will serve as a guide for students aiming to enter the field of finance, due to the changing nature of the industry and the prioritization of data analytics skills, Roberts said.
The promises and challenges of artificial intelligence and machine learning highlighted the Oct. 9 MIT Materials Day Symposium, with presentations on new ways of forming zeolite compounds, faster drug synthesis, advanced optical devices, and more.
“Machine learning is having an impact in all areas of materials research,” Materials Research Laboratory Director Carl V. Thompson said.
“We’re increasingly able to work in tandem with machines to help us decide what materials to make,” said Elsa A. Olivetti, the Atlantic Richfield Associate Professor of Energy Studies. Machine learning is also guiding how to make those materials with new insights into synthesis methods, and, in some cases (such as with robotic systems), actually making those materials, she noted.
The collaboration between the UFW Foundation and DataKind San Francisco was undertaken with the goal of providing the UFW Foundation an understanding of their constitutes’ risks and needs through the lens of data. DataKind San Francisco was tasked with finding trends and insights into ten years of immigration and public benefit data collected on immigrants and farm workers in rural California.
Due to the sensitive nature of the data, the content of the analysis will not be shared. In lieu of sharing the analysis, we’d like to share the outcomes achieved in the analysis and in what way the analysis serves the needs of the UFW Foundation.
We set out to gain insights into constituents of the UFW Foundation in order to understand the challenges they’re facing.
The joint study from the Organization for Economic Cooperation and Development and Bloomberg Philanthropies also found that urban innovation, from easing housing ordinances to attacking homelessness, can’t thrive without strong civic leadership and quality data sets to guide goals and decision-making.
The report and interactive map show “how cities are innovating, the policy areas where they are applying these approaches, and the types of roles funded within City Hall that are dedicated to innovation,” says Andrea Coleman, leader of the Government and Innovation desk at Bloomberg Philanthropies. “It also establishes a common language and a clear baseline to measure progress in cities.”
The goal of sex and gender analysis is to promote rigorous, reproducible and responsible science. Incorporating sex and gender analysis into experimental design has enabled advancements across many disciplines, such as improved treatment of heart disease and insights into the societal impact of algorithmic bias. Here we discuss the potential for sex and gender analysis to foster scientific discovery, improve experimental efficiency and enable social equality. We provide a roadmap for sex and gender analysis across scientific disciplines and call on researchers, funding agencies, peer-reviewed journals and universities to coordinate efforts to implement robust methods of sex and gender analysis. [full text]
San Francisco November 20. “Hear from local leaders as they debate and share their outlook on building AI that advances human progress through keynotes, panels, and fireside chats.” [$$]
There are lots of laws which people discuss when talking about development. This repository is a reference and overview of some of the most common ones. Please share and submit PRs!
Data, unlike some wines, do not improve with age. The contrary view, that data are immortal, a view that may underlie the often-observed tendency to recycle old examples in texts and presentations, is illustrated with three classical examples and rebutted by further examination. Some general lessons for data science are noted, as well as some history of statistical worries about the effect of data selection on induction and related themes in recent histories of science.
Medium, Netflix Tech Blog, Artem Shtatnov and Ravi Srinivas Ranganathan
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“There are plenty of existing resources describing how to express a search query in GraphQL and paginate the results. This post looks at the other side of search: how to index data and make it searchable. Specifically, how our team uses the relationships and schemas defined within GraphQL to automatically build and maintain a search database.”