Stefaan Verhulst

Martin Echenique and Luis Melgar at CityLab: “It is well known that the U.S. Census Bureau keeps track of state-to-state migration flows. But that’s not the case with Puerto Rico. Most of the publicly known numbers related to the post-Maria diaspora from the island to the continental U.S. were driven by estimates, and neither state nor federal institutions kept track of how many Puerto Ricans have left (or returned) after the storm ravaged the entire territory last September.

But Teralytics, a New York-based tech company with offices in Zurich and Singapore, has developed a map that reflects exactly how, when, and where Puerto Ricans have moved between August 2017 and February 2018. They did it by tracking data that was harvested from a sample of nearly 500,000 smartphones in partnership with one major undisclosed U.S. cell phone carrier….

The usefulness of this kind of geo-referenced data is clear in disaster relief efforts, especially when it comes to developing accurate emergency planning and determining when and where the affected population is moving.

“Generally speaking, people have their phones with them the entire time. This tells you where people are, where they’re going to, coming from, and movement patterns,” said Steven Bellovin, a computer science professor at Columbia University and former chief technologist for the U.S. Federal Trade Commission. “It could be very useful for disaster-relief efforts.”…(More)”.

Paper by Guido Noto La Diega: “Nowadays algorithms can decide if one can get a loan, is allowed to cross a border, or must go to prison. Artificial intelligence techniques (natural language processing and machine learning in the first place) enable private and public decision-makers to analyse big data in order to build profiles, which are used to make decisions in an automated way.

This work presents ten arguments against algorithmic decision-making. These revolve around the concepts of ubiquitous discretionary interpretation, holistic intuition, algorithmic bias, the three black boxes, psychology of conformity, power of sanctions, civilising force of hypocrisy, pluralism, empathy, and technocracy.

The lack of transparency of the algorithmic decision-making process does not stem merely from the characteristics of the relevant techniques used, which can make it impossible to access the rationale of the decision. It depends also on the abuse of and overlap between intellectual property rights (the “legal black box”). In the US, nearly half a million patented inventions concern algorithms; more than 67% of the algorithm-related patents were issued over the last ten years and the trend is increasing.

To counter the increased monopolisation of algorithms by means of intellectual property rights (with trade secrets leading the way), this paper presents three legal routes that enable citizens to ‘open’ the algorithms.

First, copyright and patent exceptions, as well as trade secrets are discussed.

Second, the GDPR is critically assessed. In principle, data controllers are not allowed to use algorithms to take decisions that have legal effects on the data subject’s life or similarly significantly affect them. However, when they are allowed to do so, the data subject still has the right to obtain human intervention, to express their point of view, as well as to contest the decision. Additionally, the data controller shall provide meaningful information about the logic involved in the algorithmic decision.

Third, this paper critically analyses the first known case of a court using the access right under the freedom of information regime to grant an injunction to release the source code of the computer program that implements an algorithm.

Only an integrated approach – which takes into account intellectual property, data protection, and freedom of information – may provide the citizen affected by an algorithmic decision of an effective remedy as required by the Charter of Fundamental Rights of the EU and the European Convention on Human Rights….(More)”.

Civic Data Design Lab at UrbanNext: “Ghost Cities are vacant neighborhoods and sometimes whole cities that were built but were never inhabited. Their existence is a physical manifestation of Chinese overdevelopment in real estate and the dependence on housing as an investment strategy. Little data exists which establishes the location and extent of these Ghost Cities in China. MIT’s Civic Data Design Lab developed a model using data scraped from Chinese social media sites and Baidu (Chinese Google Maps) to create one of the first maps identifying the locations of Chinese Ghost Cities….

Quantifying the extent and location of Ghost Cities is complicated by the fact that the Chinese government keeps a tight hold on data about sales and occupancy of buildings. Even local planners may have a hard time acquiring it. The Civic Data Design Lab developed a model to identify Ghost Cities based on the idea that amenities (grocery stores, hair salons, restaurants, schools, retail, etc.) are the mark of a healthy community and the lack of amenities might indicate locations where no one lives. Given the lack of openly available data in China, data was scraped from Chinese social media and websites, including Dianping (Chinese Yelp), Amap (Chinese Map Quest), Fang (Chinese Zillow), and Baidu (Chinese Google Maps) using openly accessible Application Programming Interfaces(APIs). 

Using data scraped from social media sites in Chengdu and Shenyang, the model was tested using 300 m x 300 m grid cells marking residential locations. Each grid cell was given an amenity accessibility score based on the distance and clustering of amenities nearby. Residential areas that had a cluster of low scores were marked as Ghost Cities. The results were ground-truthed through site visits documenting the location using aerial photography from drones and interviews with local stakeholders.

The model worked well at documenting under-utilized residential locations in these Chinese cities, picking up everything from vacant housing and stalled construction to abandoned older residential locations, creating the first data set that marks risk in the Chinese real estate market. The research shows that data available through social media can help locate and estimate risk in the Chinese real estate market. Perhaps more importantly, however, identifying where these areas are concentrated can help city planners, developers and local citizens make better investment decisions and address the risk created by these under-utilized developments….(More)”.

Mevan Babakar at NiemanLab: “We foolishly thought that harnessing the crowd was going to require fewer human resources, when in fact it required, at least at the micro level, more.”….There’s no end to the need for fact-checking, but fact-checking teams are usually small and struggle to keep up with the demand. In recent months, organizations like WikiTribune have suggested crowdsourcing as an attractive, low-cost way that fact-checking could scale.

As the head of automated fact-checking at the U.K.’s independent fact-checking organization Full Fact, I’ve had a lot of time to think about these suggestions, and I don’t believe that crowdsourcing can solve the fact-checking bottleneck. It might even make it worse. But — as two notable attempts, TruthSquad and FactcheckEU, have shown — even if crowdsourcing can’t help scale the core business of fact checking, it could help streamline activities that take place around it.

Think of crowdsourced fact-checking as including three components: speed (how quickly the task can be done), complexity (how difficult the task is to perform; how much oversight it needs), and coverage (the number of topics or areas that can be covered). You can optimize for (at most) two of these at a time; the third has to be sacrificed.

High-profile examples of crowdsourcing like Wikipedia, Quora, and Stack Overflow harness and gather collective knowledge, and have proven that large crowds can be used in meaningful ways for complex tasks across many topics. But the tradeoff is speed.

Projects like Gender Balance (which asks users to identify the gender of politicians) and Democracy Club Candidates (which crowdsources information about election candidates) have shown that small crowds can have a big effect when it comes to simple tasks, done quickly. But the tradeoff is broad coverage.

At Full Fact, during the 2015 U.K. general election, we had 120 volunteers aid our media monitoring operation. They looked through the entire media output every day and extracted the claims being made. The tradeoff here was that the task wasn’t very complex (it didn’t need oversight, and we only had to do a few spot checks).

But we do have two examples of projects that have operated at both high levels of complexity, within short timeframes, and across broad areas: TruthSquad and FactCheckEU….(More)”.

Tarun Khanna at Harvard Business Review: “Drones, originally developed for military purposes, weren’t approved for commercial use in the United States until 2013. When that happened, it was immediately clear that they could be hugely useful to a whole host of industries—and almost as quickly, it became clear that regulation would be a problem. The new technology raised multiple safety and security issues, there was no consensus on who should write rules to mitigate those concerns, and the knowledge needed to develop the rules didn’t yet exist in many cases. In addition, the little flying robots made a lot of people nervous.

Such regulatory, logistical, and social barriers to adopting novel products and services are very common. In fact, technology routinely outstrips society’s ability to deal with it. That’s partly because tech entrepreneurs are often insouciant about the legal and social issues their innovations birth. Although electric cars are subsidized by the federal government, Tesla has run afoul of state and local regulations because it bypasses conventional dealers to sell directly to consumers. Facebook is only now facing up to major regulatory concerns about its use of data, despite being massively successful with users and advertisers.

It’s clear that even as innovations bring unprecedented comfort and convenience, they also threaten old ways of regulating industries, running a business, and making a living. This has always been true. Thus early cars weren’t allowed to go faster than horses, and some 19th-century textile workers used sledgehammers to attack the industrial machinery they feared would displace them. New technology can even upend social norms: Consider how dating apps have transformed the way people meet.

Entrepreneurs, of course, don’t really care that the problems they’re running into are part of a historical pattern. They want to know how they can manage—and shorten—the period between the advent of a technology and the emergence of the rules and new behaviors that allow society to embrace its possibilities.

Interestingly, the same institutional murkiness that pervades nascent industries such as drones and driverless cars is something I’ve also seen in developing countries. And strange though this may sound, I believe that tech entrepreneurs can learn a lot from businesspeople who have succeeded in the world’s emerging markets.

Entrepreneurs in Brazil or Nigeria know that it’s pointless to wait for the government to provide the institutional and market infrastructure their businesses need, because that will simply take too long. They themselves must build support structures to compensate for what Krishna Palepu and I have referred to in earlier writings as “institutional voids.” They must create the conditions that will allow them to create successful products or services.

Tech-forward entrepreneurs in developed economies may want to believe that it’s not their job to guide policy makers and the public—but the truth is that nobody else can play that role. They may favor hardball tactics, getting ahead by evading rules, co-opting regulators, or threatening to move overseas. But in the long term, they’d be wiser to use soft power, working with a range of partners to co-create the social and institutional fabric that will support their growth—as entrepreneurs in emerging markets have done.…(More)”.

IADB paper by Dargent, Eduardo; Lotta, Gabriela; Mejía-Guerra, José Antonio; Moncada, Gilberto: “Why is there such heterogenity in the level of technical and institutional capacity in national statistical offices (NSOs)? Although there is broad consensus about the importance of statistical information as an essential input for decision making in the public and private sectors, this does not generally translate into a recognition of the importance of the institutions responsible for the production of data. In the context of the role of NSOs in government and society, this study seeks to explain the variation in regional statistical capacity by comparing historical processes and political economy factors in 10 Latin American countries. To do so, it proposes a new theoretical and methodological framework and offers recommendations to strengthen the institutionality of NSOs….(More)”.

Daniel Funke at Poynter: “For a brief moment, the California Republican Party supported Nazism. At least, that’s what Google said.

That’s because someone vandalized the Wikipedia page for the party on May 31 to list “Nazism” alongside ideologies like “Conservatism,” “Market liberalism” and “Fiscal conservatism.” The mistake was removed from search results, with Google clarifying to Vice News that the search engine had failed to catch the vandalism in the Wikipedia entry….

Google has long drawn upon the online encyclopedia for appending basic information to search results. According to the edit log for the California GOP page, someone added “Nazism” to the party’s ideology section around 7:40 UTC on May 31. The edit was removed within a minute, but it appears Google’s algorithm scraped the page just in time for the fake.

“Sometimes people vandalize public information sources, like Wikipedia, which can impact the information that appears in search,” a Google spokesperson told Poynter in an email. “We have systems in place that catch vandalism before it impacts search results, but occasionally errors get through, and that’s what happened here.”…

According to Google, more than 99.9 percent of Wikipedia edits that show up in Knowledge Panels, which display basic information about searchable keywords at the top of results, aren’t vandalism. The user who authored the original edit to the California GOP’s page did not use a user profile, making them hard to track down.

That’s a common tactic among people who vandalize Wikipedia pages, a practice the nonprofit has documented extensively. But given the volume of edits that are made on Wikipedia — about 10 per second, with 600 new pages per day — and the fact that Facebook and YouTube are now pulling from them to provide more context to posts, the potential for and effect of abuse is high….(More)”.

Center for Open Science: “The use of preprint servers by scholarly communities is definitely on the rise. Many developments in the past year indicate that preprints will be a huge part of the research landscape. Developments with DOIs, changes in funder expectations, and the launch of many new services indicate that preprints will become much more pervasive and reach beyond the communities where they started.

From funding agencies that want to realize impact from their efforts sooner to researchers’ desire to disseminate their research more quickly, the growth of these servers and the number of works being shared, has been substantial. At COS, we already host twenty different organizations’ services via the OSF Preprints platform.

So what’s a preprint and what is it good for? A preprint is a manuscript submitted to a  dedicated repository (like OSF Preprints, PeerJ, bioRxiv or arXiv) prior to peer review and formal publication. Some of those repositories may also accept other types of research outputs, like working papers and posters or conference proceedings. Getting a preprint out there has a variety of benefits for authors other stakeholders in the research:

• They increase the visibility of research, and sooner. While traditional papers can languish in the peer review process for months, even years, a preprint is live the minute it is submitted and moderated (if the service moderates). This means your work gets indexed by Google Scholar and Altmetric, and discovered by more relevant readers than ever before.
• You can get feedback on your work and make improvements prior to journal submission. Many authors have publicly commented about the recommendations for improvements they’ve received on their preprint that strengthened their work and even led to finding new collaborators.
• Papers with an accompanying preprint get cited 30% more often than papers without. This research from PeerJsums it up, but that’s a big benefit for scholars looking to get more visibility and impact from their efforts.
• Preprints get a permanent DOI, which makes them part of the freely accessible scientific record forever. This means others can relay on that permanence when citing your work in their research. It also means that your idea, developed by you, has a “stake in the ground” where potential scooping and intellectual theft are concerned.

So, preprints can really help lubricate scientific progress. But there are some things to keep in mind before you post. Usually, you can’t post a preprint of an article that’s already been submitted to a journal for peer review. Policies among journals vary widely, so it’s important to check with the journal you’re interested in sending your paper to BEFORE you submit a preprint that might later be published. A good resource for doing this is JISC’s SHERPA/RoMEO database. It’s also a good idea to understand the licensing choices available. At OSF Preprints, we recommend the CC-BY license suite, but you can check choosealicense.com or https://osf.io/6uupa/ for good overviews on how best to license your submissions….(More)”.

Press Release: “Results for Development (R4D) has released a new study unpacking how evidence translators play a key and somewhat surprising role in ensuring policymakers have the evidence they need to make informed decisions. Translators — who can be evidence producers, policymakers, or intermediaries such as journalists, advocates and expert advisors — identify, filter, interpret, adapt, contextualize and communicate data and evidence for the purposes of policymaking.

The study, Translators’ Role in Evidence-Informed Policymaking, provides a better understanding of who translators are and how different factors influence translators’ ability to promote the use of evidence in policymaking. This research shows translation is an essential function and that, absent individuals or organizations taking up the translator role, evidence translation and evidence-informed policymaking often do not take place.

“We began this research assuming that translators’ technical skills and analytical prowess would prove to be among the most important factors in predicting when and how evidence made its way into public sector decision making,” Nathaniel Heller, executive vice president for integrated strategies at Results for Development, said. “Surprisingly, that turned out not to be the case, and other ‘soft’ skills play a far larger role in translators’ efficacy than we had imagined.”

Key findings include:

• Translator credibility and reputation are crucial to the ability to gain access to policymakers and to promote the uptake of evidence.
• Political savvy and stakeholder engagement are among the most critical skills for effective translators.
• Conversely, analytical skills and the ability to adapt, transform and communicate evidence were identified as being less important stand-alone translator skills.
• Evidence translation is most effective when initiated by those in power or when translators place those in power at the center of their efforts.

The study includes a definitional and theoretical framework as well as a set of research questions about key enabling and constraining factors that might affect evidence translators’ influence. It also focuses on two cases in Ghana and Argentina to validate and debunk some of the intellectual frameworks around policy translators that R4D and others in the field have already developed. The first case focuses on Ghana’s blue-ribbon commission formed by the country’s president in 2015, which was tasked with reviewing Ghana’s national health insurance scheme. The second case looks at Buenos Aires’ 2016 government-led review of the city’s right to information regime….(More)”.