Stefaan Verhulst

Article by Carlo Rovelli: “In the institute where I used to work a few years ago, a rare non-infectious illness hit five colleagues in quick succession. There was a sense of alarm, and a hunt for the cause of the problem. In the past the building had been used as a biology lab, so we thought that there might be some sort of chemical contamination, but nothing was found. The level of apprehension grew. Some looked for work elsewhere.

One evening, at a dinner party, I mentioned these events to a friend who is a mathematician, and he burst out laughing. “There are 400 tiles on the floor of this room; if I throw 100 grains of rice into the air, will I find,” he asked us, “five grains on any one tile?” We replied in the negative: there was only one grain for every four tiles: not enough to have five on a single tile.

We were wrong. We tried numerous times, actually throwing the rice, and there was always a tile with two, three, four, even five or more grains on it. Why? Why would grains “flung randomly” not arrange themselves into good order, equidistant from each other?

Because they land, precisely, by chance, and there are always disorderly grains that fall on tiles where others have already gathered. Suddenly the strange case of the five ill colleagues seemed very different. Five grains of rice falling on the same tile does not mean that the tile possesses some kind of “rice-­attracting” force. Five people falling ill in a workplace did not mean that it must be contaminated. The institute where I worked was part of a university. We, know-­all professors, had fallen into a gross statistical error. We had become convinced that the “above average” number of sick people required an explanation. Some had even gone elsewhere, changing jobs for no good reason.

Life is full of stories such as this. Insufficient understanding of statistics is widespread. The current pandemic has forced us all to engage in probabilistic reasoning, from governments having to recommend behaviour on the basis of statistical predictions, to people estimating the probability of catching the virus while taking part in common activities. Our extensive statistical illiteracy is today particularly dangerous.

We use probabilistic reasoning every day, and most of us have a vague understanding of averages, variability and correlations. But we use them in an approximate fashion, often making errors. Statistics sharpen and refine these notions, giving them a precise definition, allowing us to reliably evaluate, for instance, whether a medicine or a building is dangerous or not.

Society would gain significant advantages if children were taught the fundamental ideas of probability theory and statistics: in simple form in primary school, and in greater depth in secondary school….(More)”.

Paper by David Leslie: “Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems on impacted individuals and communities.

Opponents argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threatens to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. They also caution that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation. Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring.

In this explainer, I focus on one central aspect of this debate: the role that dynamics of bias and discrimination play in the development and deployment of FDRTs. I examine how historical patterns of discrimination have made inroads into the design and implementation of FDRTs from their very earliest moments. And, I explain the ways in which the use of biased FDRTs can lead distributional and recognitional injustices. I also describe how certain complacent attitudes of innovators and users toward redressing these harms raise serious concerns about expanding future adoption. The explainer concludes with an exploration of broader ethical questions around the potential proliferation of pervasive face-based surveillance infrastructures and makes some recommendations for cultivating more responsible approaches to the development and governance of these technologies….(More)”.

Report by the Joint Research Center (EU): “…The report analyses the cognitive challenges posed by four pressure points: attention economy, platform choice architectures, algorithmic content curation and disinformation, and makes policy recommendations to address them.

Specific actions could include banning microtargeting for political ads, transparency rules so that users understand how an algorithm uses their data and to what effect, or requiring online platforms to provide reports to users showing when, how and which of their data is sold.

This report is the second output from the JRC’s Enlightenment 2.0 multi-annual research programme….(More)”.

A Study Based on Crowdlaw—Online Public Participation in Lawmaking – by Marciele Berger Bernardes, Francisco Pacheco de Andrade and Paulo Novais: “The advent of Information and Communication Technologies (ICTs) brought a fast development of urban centers, and a debate emerges on how to use ICTs to enhance the development and quality of life in cities and how to make these more efficient. …

This way, along with the prominent literature and the experience of good international practices, we must recognize the need for an “intelligent” regulatory modeling thus being, we presented a contribution to building a new legal paradigm toward the enhancement of democratic processes in smart cities, structured on the postulates of Crowdlaw (collective production of the legislative process). Last, we believe that the contributions arising out of this work may fill some of the gaps existing in terms of legal theory production on the regulatory modeling for participative governance….(More)”.

Paper by Elizabeth A. Evans, Elizabeth Delorme, Karl Cyr & Daniel M. Goldstein: “The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms….

We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health.

Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches.

Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions….(More)”

Paper by Alevtina Krotova, Armin Mertens, Marc Scheufen: “Data is an important business resource. It forms the basis for various digital technologies such as artificial intelligence or smart services. However, access to data is unequally distributed in the market. Hence, some business ideas fail due to a lack of data sources. Although many governments have recognised the importance of open data and already make administrative data available to the public on a large scale, many companies are still reluctant to share their data among other firms and competitors. As a result, the economic potential of data is far from being fully exploited. Against this background, we analyse current developments in the area of open data. We compare the characteristics of open governmental and open company data in order to define the necessary framework conditions for data sharing. Subsequently, we examine the status quo of data sharing among firms. We use a qualitative analysis of survey data of European companies to derive the sufficient conditions to strengthen data sharing. Our analysis shows that governmental data is a public good, while company data can be seen as a club or private good. Latter frequently build the core for companies’ business models and hence are less suitable for data sharing. Finally, we find that promoting legal certainty and the economic impact present important policy steps for fostering data sharing….(More)”

Press Release: “American consumers are increasingly concerned about privacy and data security when purchasing new products and services, which may be a competitive advantage to companies that take action towards these consumer values, a new Consumer Reports study finds. 

The new study, “Privacy Front and Center” from CR’s Digital Lab with support from Omidyar Network, looks at the commercial benefits for companies that differentiate their products based on privacy and data security. The study draws from a nationally representative CR survey of 5,085 adult U.S. residents conducted in February 2020, a meta-analysis of 25 years of public opinion studies, and a conjoint analysis that seeks to quantify how consumers weigh privacy and security in their hardware and software purchasing decisions. 

“This study shows that raising the standard for privacy and security is a win-win for consumers and the companies,” said Ben Moskowitz, the director of the Digital Lab at Consumer Reports. “Given the rapid proliferation of internet connected devices, the rise in data breaches and cyber attacks, and the demand from consumers for heightened privacy and security measures, there’s an undeniable business case for companies to invest in creating more private and secure products.” 

Here are some of the key findings from the study:

• According to CR’s February 2020 nationally representative survey, 74% of consumers are at least moderately concerned about the privacy of their personal data.
• Nearly all Americans (96%) agree that more should be done to ensure that companies protect the privacy of consumers.
• A majority of smart product owners (62%) worry about potential loss of privacy when buying them for their home or family.
• The privacy/security conscious consumer class seems to include more men and people of color.
• Experiencing a data breach correlates with a higher willingness to pay for privacy, and 30% of Americans have experienced one.
• Of the Android users who switched to iPhones, 32% indicated doing so because of Apple’s perceived privacy or security benefits relative to Android….(More)”.

Report by Lewis Lloyd: “…Policy makers across government lack the necessary skills and understanding to take advantage of digital technologies when tackling problems such as coronavirus and climate change. This report says already poor data management has been exacerbated by a lack of leadership, with the role of government chief data officer unfilled since 2017. These failings have been laid bare by the stuttering coronavirus Test and Trace programme. Drawing on interviews with policy experts and digital specialists inside and outside government, the report argues that better use of data and new technologies, such as artificial intelligence, would improve policy makers’ understanding of problems like coronavirus and climate change, and aid collaboration with colleagues, external organisations and the public in seeking solutions to them. It urges government to trial innovative applications of data and technology to ​a wider range of policies, but warns recent failures such as the A-level algorithm fiasco mean it must also do more to secure public trust in its use of such technologies. This means strengthening oversight and initiating a wider public debate about the appropriate use of digital technologies, and improving officials’ understanding of the limitations of data-driven analysis. The report recommends that the government:

1. Appoints a chief data officer as soon as possible to drive work on improving data quality, tackle problems with legacy IT and make sure new data standards are applied and enforced across government.
2. ​Places more emphasis on statistical and technological literacy when recruiting and training policy officials.
3. Sets up a new independent body to lead on public engagement in policy making, with an initial focus on how and when government should use data and technology…(More)”.

Blog by Salomé Viljoen: “Since the proliferation of the World Wide Web in the 1990s, critics of widely used internet communications services have warned of the misuse of personal data. Alongside familiar concerns regarding user privacy and state surveillance, a now-decades-long thread connects a group of theorists who view data—and in particular data about people—as central to what they have termed informational capitalism.¹ Critics locate in datafication—the transformation of information into commodity—a particular economic process of value creation that demarcates informational capitalism from its predecessors. Whether these critics take “information” or “capitalism” as the modifier warranting primary concern, datafication, in their analysis, serves a dual role: both a process of production and a form of injustice.

In arguments levied against informational capitalism, the creation, collection, and use of data feature prominently as an unjust way to order productive activity. For instance, in her 2019 blockbuster The Age of Surveillance Capitalism, Shoshanna Zuboff likens our inner lives to a pre-Colonial continent, invaded and strip-mined of data by technology companies seeking profits.² Elsewhere, Jathan Sadowski identifies data as a distinct form of capital, and accordingly links the imperative to collect data to the perpetual cycle of capital accumulation.³ Julie Cohen, in the Polanyian tradition, traces the “quasi-ownership through enclosure” of data and identifies the processing of personal information in “data refineries” as a fourth factor of production under informational capitalism.⁴

Critiques breed proposals for reform. Thus, data governance emerges as key terrain on which to discipline firms engaged in datafication and to respond to the injustices of informational capitalism. Scholars, activists, technologists and even presidential candidates have all proposed data governance reforms to address the social ills generated by the technology industry.

These reforms generally come in two varieties. Propertarian reforms diagnose the source of datafication’s injustice in the absence of formal property (or alternatively, labor) rights regulating the process of production. In 2016, inventor of the world wide web Sir Tim Berners-Lee founded Solid, a web decentralization platform, out of his concern over how data extraction fuels the growing power imbalance of the web which, he notes, “has evolved into an engine of inequity and division; swayed by powerful forces who use it for their own agendas.” In response, Solid “aims to radically change the way Web applications work today, resulting in true data ownership as well as improved privacy.” Solid is one popular project within the blockchain community’s #ownyourdata movement; another is Radical Markets, a suite of proposals from Glen Weyl (an economist and researcher at Microsoft) that includes developing a labor market for data. Like Solid, Weyl’s project is in part a response to inequality: it aims to disrupt the digital economy’s “technofeudalism,” where the unremunerated fruits of data laborers’ toil help drive the inequality of the technology economy writ large.⁵ Progressive politicians from Andrew Yang to Alexandria Ocasio-Cortez have similarly advanced proposals to reform the information economy, proposing variations on the theme of user-ownership over their personal data.

The second type of reforms, which I call dignitarian, take a further step beyond asserting rights to data-as-property, and resist data’s commodification altogether, drawing on a framework of civil and human rights to advocate for increased protections. Proposed reforms along these lines grant individuals meaningful capacity to say no to forms of data collection they disagree with, to determine the fate of data collected about them, and to grant them rights against data about them being used in ways that violate their interests….(More)”.

Book by Sarah Brayne: “The scope of criminal justice surveillance has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, healthcare, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences.

In Predict and Surveil, Sarah Brayne offers an unprecedented, inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world-the Los Angeles Police Department. Drawing on original interviews and ethnographic observations, Brayne examines the causes and consequences of algorithmic control. She reveals how the police use predictive analytics to deploy resources, identify suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-intensive practices. Although big data analytics holds potential to reduce bias and increase efficiency, Brayne argues that it also reproduces and deepens existing patterns of social inequality, threatens privacy, and challenges civil liberties.

A groundbreaking examination of the growing role of the private sector in public policing, this book challenges the way we think about the data-heavy supervision law enforcement increasingly imposes upon civilians in the name of objectivity, efficiency, and public safety….(More)”.