Stefaan Verhulst

Paper by Jessica Feldman:”This scoping paper considers how digital tools, such as ICTs and AI, have failed to contribute to the “common good” in any sustained or scalable way. This is attributed to a problem that is at once political-economic and technical.

Many digital tools’ business models are predicated on advertising: framing the user as an individual consumer-to-be-targeted, not as an organization, movement, or any sort of commons. At the level of infrastructure and hardware, the increased privatization and centralization of transmission and production leads to a dangerous bottlenecking of communication power, and to labor and production practices that are undemocratic and damaging to common resources.

These practices escalate collective action problems, pose a threat to democratic decision making, aggravate issues of economic and labor inequality, and harm the environment and health. At the same time, the growth of both AI and online community formation raise questions around the very definition of human subjectivity and modes of relationality. Based on an operational definition of the common good grounded in ethics of care, sustainability, and redistributive justice, suggestions are made for solutions and further research in the areas of participatory design, digital democracy, digital labor, and environmental sustainability….(More)”

Article by Clive Thompson: “…When the open source concept emerged in the ’90s, it was conceived as a bold new form of communal labor: digital barn raisings. If you made your code open source, dozens or even hundreds of programmers would chip in to improve it. Many hands would make light work. Everyone would feel ownership.

Now, it’s true that open source has, overall, been a wild success. Every startup, when creating its own software services or products, relies on open source software from folks like Thornton: open source web-server code, open source neural-net code. But, with the exception of some big projects—like Linux—the labor involved isn’t particularly communal. Most are like Bootstrap, where the majority of the work landed on a tiny team of people.

Recently, Nadia Eghbal—the head of writer experience at the email newsletter platform Substack—published Working in Public, a fascinating book for which she spoke to hundreds of open source coders. She pinpointed the change I’m describing here. No matter how hard the programmers worked, most “still felt underwater in some shape or form,” Eghbal told me.

Why didn’t the barn-raising model pan out? As Eghbal notes, it’s partly that the random folks who pitch in make only very small contributions, like fixing a bug. Making and remaking code requires a lot of high-level synthesis—which, as it turns out, is hard to break into little pieces. It lives best in the heads of a small number of people.

Yet those poor top-level coders still need to respond to the smaller contributions (to say nothing of requests for help or reams of abuse). Their burdens, Eghbal realized, felt like those of YouTubers or Instagram influencers who feel overwhelmed by their ardent fan bases—but without the huge, ad-based remuneration.

Sometimes open source coders simply walk away: Let someone else deal with this crap. Studies suggest that about 9.5 percent of all open source code is abandoned, and a quarter is probably close to being so. This can be dangerous: If code isn’t regularly updated, it risks causing havoc if someone later relies on it. Worse, abandoned code can be hijacked for ill use. Two years ago, the pseudonymous coder right9ctrl took over a piece of open source code that was used by bitcoin firms—and then rewrote it to try to steal cryptocurrency….(More)”.

Article by Justine Calma: “Google unveiled a tool today that could help cities keep their residents cool by mapping out where trees are needed most. Cities tend to be warmer than surrounding areas because buildings and asphalt trap heat. An easy way to cool metropolitan areas down is to plant more trees in neighborhoods where they’re sparse.

Google’s new Tree Canopy Lab uses aerial imagery and Google’s AI to figure out where every tree is in a city. Tree Canopy Lab puts that information on an interactive map along with additional data on which neighborhoods are more densely populated and are more vulnerable to high temperatures. The hope is that planting new trees in these areas could help cities adapt to a warming world and save lives during heat waves.

Google piloted Tree Canopy Lab in Los Angeles. Data on hundreds more cities is on the way, the company says. City planners interested in using the tool in the future can reach out to Google through a form it posted along with today’s announcement.

“We’ll be able to really home in on where the best strategic investment will be in terms of addressing that urban heat,” says Rachel Malarich, Los Angeles’ first city forest officer.

Google claims that its new tool can save cities like Los Angeles time when it comes to taking inventory of their trees. That’s often done by sending people to survey each block. Los Angeles has also used LIDAR technology to map their urban forest in the past, which uses a laser sensor to detect the trees — but that process was expensive and slow, according to Malarich. Google’s new service, on the other hand, is free to use and will be updated regularly using images the company already takes by plane for Google Maps….(More)”.

Policy Brief by Lara Mangravite and John Wilbanks: “Open data mandates and investments in public data resources, such as the Human Genome Project or the U.S. National Oceanic and Atmospheric Administration Data Discovery Portal, have provided essential data sets at a scale not possible without government support. By responsibly sharing data for wide reuse, federal policy can spur innovation inside the academy and in citizen science communities. These approaches are enabled by private-sector advances in cloud computing services and the government has benefited from innovation in this domain. However, the use of commercial products to manage the storage of and access to public data resources poses several challenges.

First, too many cloud computing systems fail to properly secure data against breaches, improperly share copies of data with other vendors, or use data to add to their own secretive and proprietary models. As a result, the public does not trust technology companies to responsibly manage public data—particularly private data of individual citizens. These fears are exacerbated by the market power of the major cloud computing providers, which may limit the ability of individuals or institutions to negotiate appropriate terms. This impacts the willingness of U.S. citizens to have their personal information included within these databases.

Second, open data solutions are springing up across multiple sectors without coordination. The federal government is funding a series of independent programs that are working to solve the same problem, leading to a costly duplication of effort across programs.

Third and most importantly, the high costs of data storage, transfer, and analysis preclude many academics, scientists, and researchers from taking advantage of governmental open data resources. Cloud computing has radically lowered the costs of high-performance computing, but it is still not free. The cost of building the wrong model at the wrong time can quickly run into tens of thousands of dollars.

Scarce resources mean that many academic data scientists are unable or unwilling to spend their limited funds to reuse data in exploratory analyses outside their narrow projects. And citizen scientists must use personal funds, which are especially scarce in communities traditionally underrepresented in research. The vast majority of public data made available through existing open science policy is therefore left unused, either as reference material or as “foreground” for new hypotheses and discoveries….The Solution: Public Cloud Computing…(More)”.

Paper by Khaled El Emam et al: “There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.

Objective: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.

Methods: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this “meaningful identity disclosure risk.” The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data.

Results: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively.

Conclusions: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data….(More)”.

Ben Moscovitch at Pew: “…The Office of the National Coordinator for Health Information Technology (ONC) will collect clinicians’ feedback through a survey developed by the Urban Institute under a contract with the agency. ONC will release aggregated results as part its EHR reporting program. Congress required the program’s creation in the 21st Century Cures Act, the wide-ranging federal health legislation enacted in 2016. The act directs ONC to determine which data to gather from health information technology vendors. That information can then be used to illuminate the strengths and weaknesses of EHR products, as well as industry trends.

The Pew Charitable Trusts, major medical organizations and hospital groups, and health information technology experts have urged that the reporting program examine usability-related patient risks. Confusing, cumbersome, and poorly customized EHR systems can cause health care providers to order the wrong drug or miss test results and other information critical to safe, effective treatment. Usability challenges also can increase providers’ frustration and, in turn, their likelihood of making mistakes.

The data collected from clinicians will shed light on these problems, encourage developers to improve the safety of their products, and help hospitals and doctor’s offices make better-informed decisions about the purchase, implementation, and use of these tools. Research shows that aggregated data about EHRs can generate product-specific insights about safety deficiencies, even when health care facilities implement the same system in distinct ways….(More)”.

Joseph Cox at Vice: “The U.S. military is buying the granular movement data of people around the world, harvested from innocuous-seeming apps, Motherboard has learned. The most popular app among a group Motherboard analyzed connected to this sort of data sale is a Muslim prayer and Quran app that has more than 98 million downloads worldwide. Others include a Muslim dating app, a popular Craigslist app, an app for following storms, and a “level” app that can be used to help, for example, install shelves in a bedroom.

Through public records, interviews with developers, and technical analysis, Motherboard uncovered two separate, parallel data streams that the U.S. military uses, or has used, to obtain location data. One relies on a company called Babel Street, which creates a product called Locate X. U.S. Special Operations Command (USSOCOM), a branch of the military tasked with counterterrorism, counterinsurgency, and special reconnaissance, bought access to Locate X to assist on overseas special forces operations. The other stream is through a company called X-Mode, which obtains location data directly from apps, then sells that data to contractors, and by extension, the military.

The news highlights the opaque location data industry and the fact that the U.S. military, which has infamously used other location data to target drone strikes, is purchasing access to sensitive data. Many of the users of apps involved in the data supply chain are Muslim, which is notable considering that the United States has waged a decades-long war on predominantly Muslim terror groups in the Middle East, and has killed hundreds of thousands of civilians during its military operations in Pakistan, Afghanistan, and Iraq. Motherboard does not know of any specific operations in which this type of app-based location data has been used by the U.S. military.

The apps sending data to X-Mode include Muslim Pro, an app that reminds users when to pray and what direction Mecca is in relation to the user’s current location. The app has been downloaded over 50 million times on Android, according to the Google Play Store, and over 98 million in total across other platforms including iOS, according to Muslim Pro’s website….(More)”.

Blog post by Bill Gates: “My family loves to do jigsaw puzzles. It’s one of our favorite activities to do together, especially when we’re on vacation. There is something so satisfying about everyone working as a team to put down piece after piece until finally the whole thing is done.

In a lot of ways, the fight against Alzheimer’s disease reminds me of doing a puzzle. Your goal is to see the whole picture, so that you can understand the disease well enough to better diagnose and treat it. But in order to see the complete picture, you need to figure out how all of the pieces fit together.

Right now, all over the world, researchers are collecting data about Alzheimer’s disease. Some of these scientists are working on drug trials aimed at finding a way to stop the disease’s progression. Others are studying how our brain works, or how it changes as we age. In each case, they’re learning new things about the disease.

But until recently, Alzheimer’s researchers often had to jump through a lot of hoops to share their data—to see if and how the puzzle pieces fit together. There are a few reasons for this. For one thing, there is a lot of confusion about what information you can and can’t share because of patient privacy. Often there weren’t easily available tools and technologies to facilitate broad data-sharing and access. In addition, pharmaceutical companies invest a lot of money into clinical trials, and often they aren’t eager for their competitors to benefit from that investment, especially when the programs are still ongoing.

Unfortunately, this siloed approach to research data hasn’t yielded great results. We have only made incremental progress in therapeutics since the late 1990s. There’s a lot that we still don’t know about Alzheimer’s, including what part of the brain breaks down first and how or when you should intervene. But I’m hopeful that will change soon thanks in part to the Alzheimer’s Disease Data Initiative, or ADDI….(More)“.

Paper by Maria Ralli et al: “The lack of granular and rich descriptive metadata highly affects the discoverability and usability of the digital content stored in museums, libraries and archives, aggregated and served through Europeana, thus often frustrating the user experience offered by these institutions’ portals. In this context, metadata enrichment services through automated analysis and feature extraction along with crowdsourcing annotation services can offer a great opportunity for improving the metadata quality of digital cultural content in a scalable way, while at the same time engaging different user communities and raising awareness about cultural heritage assets. Such an effort is Crowdheritage, an open crowdsourcing platform that aims to employ machine and human intelligence in order to improve the digital cultural content metadata quality….(More)”.

Article by Daria Gritsenko and Matthew Wood: “This article examines how modes of governance are reconfigured as a result of using algorithms in the governance process. We argue that deploying algorithmic systems creates a shift toward a special form of design‐based governance, with power exercised ex ante via choice architectures defined through protocols, requiring lower levels of commitment from governing actors. We use governance of three policy problems – speeding, disinformation, and social sharing – to illustrate what happens when algorithms are deployed to enable coordination in modes of hierarchical governance, self‐governance, and co‐governance. Our analysis shows that algorithms increase efficiency while decreasing the space for governing actors’ discretion. Furthermore, we compare the effects of algorithms in each of these cases and explore sources of convergence and divergence between the governance modes. We suggest design‐based governance modes that rely on algorithmic systems might be re‐conceptualized as algorithmic governance to account for the prevalence of algorithms and the significance of their effects….(More)”.