Stefaan Verhulst

Book by Sun-ha Hong: “What counts as knowledge in the age of big data and smart machines? In its pursuit of better knowledge, technology is reshaping what counts as knowledge in its own image – and demanding that the rest of us catch up to new machinic standards for what counts as suspicious, informed, employable. In the process, datafication often generates speculation as much as it does information. The push for algorithmic certainty sets loose an expansive array of incomplete archives, speculative judgments and simulated futures where technology meets enduring social and political problems.

Technologies of Speculation traces this technological manufacturing of speculation as knowledge. It shows how unprovable predictions, uncertain data and black-boxed systems are upgraded into the status of fact – with lasting consequences for criminal justice, public opinion, employability, and more. It tells the story of vast dragnet systems constructed to predict the next terrorist, and how familiar forms of prejudice seep into the data by the back door. In software placeholders like ‘Mohammed Badguy’, the fantasy of pure data collides with the old spectre of national purity. It tells the story of smart machines for ubiquitous and automated self-tracking, manufacturing knowledge that paradoxically lies beyond the human senses. Such data is increasingly being taken up by employers, insurers and courts of law, creating imperfect proxies through which my truth can be overruled.

The book situates ongoing controversies over AI and algorithms within a broader societal faith in objective truth and technological progress. It argues that even as datafication leverages this faith to establish its dominance, it is dismantling the longstanding link between knowledge and human reason, rational publics and free individuals. Technologies of Speculation thus emphasises the basic ethical problem underlying contemporary debates over privacy, surveillance and algorithmic bias: who, or what, has the right to the truth of who I am and what is good for me? If data promises objective knowledge, then we must ask in return: knowledge by and for whom, enabling what forms of life for the human subject?…(More)”.

Paper by Huaxiong Jiang, Stan Geertman & Patrick Witte: “This paper argues for a specific urban planning perspective on smart governance that we call “smart urban governance,” which represents a move away from the technocratic way of governing cities often found in smart cities. A framework on smart urban governance is proposed on the basis of three intertwined key components, namely spatial, institutional, and technological components. To test the applicability of the framework, we conducted an international questionnaire survey on smart city projects. We then identified and discursively analyzed two smart city projects—Smart Nation Singapore and Helsinki Smart City—to illustrate how this framework works in practice. The questionnaire survey revealed that smart urban governance varies remarkably: As urban issues differ in different contexts, the governance modes and relevant ICT functionalities applied also differ considerably. Moreover, the case analysis indicates that a focus on substantive urban challenges helps to define appropriate modes of governance and develop dedicated technologies that can contribute to solving specific smart city challenges. The analyses of both cases highlight the importance of context (cultural, political, economic, etc.) in analyzing interactions between the components. In this, smart urban governance promotes a sociotechnical way of governing cities in the “smart” era by starting with the urban issue at stake, promoting demand-driven governance modes, and shaping technological intelligence more socially, given the specific context….(More)”.

” The Governance Lab (The GovLab) at the NYU Tandon School of Engineering, with support from the Henry Luce Foundation, today released guidance to inform decision-making in the responsible re-use of data — re-purposing data for a use other than that for which it was originally intended — to address COVID-19. The findings, recommendations, and a new Responsible Data Re-Use framework stem from The Data Assembly initiative in New York City. An effort to solicit diverse, actionable public input on data re-use for crisis response in the United States, the Data Assembly brought together New York City-based stakeholders from government, the private sector, civic rights and advocacy organizations, and the general public to deliberate on innovative, though potentially risky, uses of data to inform crisis response in New York City. The findings and guidance from the initiative will inform policymaking and practice regarding data re-use in New York City, as well as free data literacy training offerings.

The Data Assembly’s Responsible Data Re-Use Framework provides clarity on a major element of the ongoing crisis. Though leaders throughout the world have relied on data to reduce uncertainty and make better decisions, expectations around the use and sharing of siloed data assets has remained unclear. This summer, along with the New York Public Library and Brooklyn Public Library, The GovLab co-hosted four months of remote deliberations with New York-based civil rights organizations, key data holders, and policymakers. Today’s release is a product of these discussions, to show how New Yorkers and their leaders think about the opportunities and risks involved in the data-driven response to COVID-19….(More)”

See: The Data Assembly Synthesis Report by y Andrew Young, Stefaan G. Verhulst, Nadiya Safonova, and Andrew J. Zahuranec

Richard Hughes Gibson at the Hedgehog Review: “In the last decade of the twentieth century, as we’ve seen, Howard Rheingold and William J. Mitchell imagined the Web as an “electronic agora” where netizens would roam freely, mixing business, pleasure, and politics. Al Gore envisioned it as an “information superhighway” system for which any computer could offer an onramp. Our current condition, by contrast, has been likened to shuffling between “walled gardens,” each platform—be it Facebook, Apple, Amazon, or Google—being its own tightly controlled ecosystem. Yet even this metaphor is perhaps too benign. As the cultural critic Alan Jacobs has observed, “they are not gardens; they are walled industrial sites, within which users, for no financial compensation, produce data which the owners of the factories sift and then sell.”

Harvard Business School professor Shoshanna Zuboff has dubbed the business model underlying these factories “surveillance capitalism.” Surveillance capitalism works by collecting information about you (your Internet activity, call history, app usage, your voice, your location, even your fitness level), which creates profiles of what you like, where you go, who you know, and who you are. That shadowy portrait makes a powerful tool for predicting what kinds of products and services you might like to purchase, and other companies are happy to pay for such finely-tuned targeted advertising. (Facebook alone generated $69 billion in ad revenue last year.)

The information-gathering can’t ever stop, however; the business model depends on a steady supply of new user data to inform the next round of predictions. This “extraction imperative,” as Zuboff calls it, is inherently monopolistic, rival companies being both a threat that must be eliminated and a potential gold mine from which more user data can be extracted (see Facebook’s acquisitions of competitors Whatsapp and Instagram). Equally worryingly, the big tech companies have begun moving into other sectors of the economy, as seen, for example, in Google’s quiet entry last year into the medical records business (unbeknownst to the patients and physicians whose data was mined).

There is growing consensus among legal scholars and social scientists that these practices are hazardous to democracy. Commentators worry over the consequences of putting so much wealth in so few hands so quickly (Zuboff calls it a “new Gilded Age”). They note the number of tech executives who’ve gone on to high-ranking government posts and vice versa. They point to the fact that—contrary to Mark Zuckerberg’s 2010 declaration that privacy is no longer a “social norm”—users are indeed worried about privacy. Scholars note, furthermore, that these platforms are not a genuine reflection of public opinion, though they are often treated as such. Social media can operate as echo chambers, only showing you what people like you read, think, do. Paradoxically, they can also become pressure cookers. As is now widely documented, many algorithms reward—and thereby amplify—the most divisive and thus most attention-grabbing content. Keeping us dialed in—whether for the next round of affirmation or outrage—is essential to their success….(More)”.

Steve Lohr at the New York Times: “L. Rafael Reif, the president of Massachusetts Institute of Technology, delivered an intellectual call to arms to the university’s faculty in November 2017: Help generate insights into how advancing technology has changed and will change the work force, and what policies would create opportunity for more Americans in the digital economy.

That issue, he wrote, is the “defining challenge of our time.”

Three years later, the task force assembled to address it is publishing its wide-ranging conclusions. The 92-page report, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” was released on Tuesday….

Here are four of the key findings in the report:

Most American workers have fared poorly.

It’s well known that those on the top rungs of the job ladder have prospered for decades while wages for average American workers have stagnated. But the M.I.T. analysis goes further. It found, for example, that real wages for men without four-year college degrees have declined 10 to 20 percent since their peak in 1980….

Robots and A.I. are not about to deliver a jobless future.

…The M.I.T. researchers concluded that the change would be more evolutionary than revolutionary. In fact, they wrote, “we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them.”…

Worker training in America needs to match the market.

“The key ingredient for success is public-private partnerships,” said Annette Parker, president of South Central College, a community college in Minnesota, and a member of the advisory board to the M.I.T. project.

The schools, nonprofits and corporate-sponsored programs that have succeeded in lifting people into middle-class jobs all echo her point: the need to link skills training to business demand….

Workers need more power, voice and representation.The report calls for raising the minimum wage, broadening unemployment insurance and modifying labor laws to enable collective bargaining in occupations like domestic and home-care workers and freelance workers. Such representation, the report notes, could come from traditional unions or worker advocacy groups like the National Domestic Workers Alliance, Jobs With Justice and the Freelancers Union….(More)”

Revised and Updated Book by Kevin Werbach and Dan Hunter on “The Power of Gamification and Game Thinking in Business, Education, Government, and Social Impact”: “For thousands of years, we’ve created things called games that tap the tremendous psychic power of fun. In a revised and updated edition of For the Win: The Power of Gamification and Game Thinking in Business, Education, Government, and Social Impact, authors Kevin Werbach and Dan Hunter argue that applying the lessons of gamification could change your business, the way you learn or teach, and even your life.

Werbach and Hunter explain how games can be used as a valuable tool to address serious pursuits like marketing, productivity enhancement, education, innovation, customer engagement, human resources, and sustainability. They reveal how, why, and when gamification works—and what not to do.

Discover the successes—and failures—of organizations that are using gamification:

• How a South Korean company called Neofect is using gamification to help people recover from strokes;
• How a tool called SuperBetter has demonstrated significant results treating depression, concussion symptoms, and the mental health harms of the COVID-19 pandemic through game thinking;
• How the ride-hailing giant Uber once used gamification to influence their drivers to work longer hours than they otherwise wanted to, causing swift backlash.

The story of gamification isn’t fun and games by any means. It’s serious. When used carefully and thoughtfully, gamification produces great outcomes for users, in ways that are hard to replicate through other methods. Other times, companies misuse the “guided missile” of gamification to have people work and do things in ways that are against their self-interest.

This revised and updated edition incorporates the most prominent research findings to provide a comprehensive gamification playbook for the real world….(More)”.

Article by Ravi Parikh et al: “When conversations about goals and end-of-life wishes happen early, they can improve patients’ quality of life and decrease their chances of dying on a ventilator or in an intensive care unit. Yet doctors treating cancer focus so much of their attention on treating the disease that these conversations tend to get put off until it’s too late. This leads to costly and often unwanted care for the patient.Related: 

This can be fixed, but it requires addressing two key challenges. The first is that it is often difficult for doctors to know how long patients have left to live. Even among patients in hospice care, doctors get it wrong nearly 70% of the time. Hospitals and private companies have invested millions of dollars to try and identify these outcomes, often using artificial intelligence and machine learning, although most of these algorithms have not been vetted in real-world settings.

In a recent set of studies, our team used data from real-time electronic medical records to develop a machine learning algorithm that identified which cancer patients had a high risk of dying in the next six months. We then tested the algorithm on 25,000 patients who were seen at our health system’s cancer practices and found it performed better than relying only on doctors to identify high-risk patients.

But just because such a tool exists doesn’t mean doctors will use it to prompt more conversations. The second challenge — which is even harder to overcome — is using machine learning to motivate clinicians to have difficult conversations with patients about the end of life.

We wondered if implementing a timely “nudge” that doctors received before seeing their high-risk patients could help them start the conversation.

To test this idea, we used our prediction tool in a clinical trial involving nine cancer practices. Doctors in the nudge group received a weekly report on how many end-of-life conversations they had compared to their peers, along with a list of patients they were scheduled to see the following week who the algorithm deemed at high-risk of dying in the next six months. They could review the list and uncheck any patients they thought were not appropriate for end-of-life conversations. For the patients who remained checked, doctors received a text message on the day of the appointment reminding them to discuss the patient’s goals at the end of life. Doctors in the control group did not receive the email or text message intervention.

As we reported in JAMA Oncology, 15% of doctors who received the nudge text had end-of-life conversations with their patients, compared to just 4% of the control doctors….(More)”.

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)”.