Official Statistics 4.0: Verified Facts for People in the 21st Century


Book by Walter J. Radermacher: “This book explores official statistics and their social function in modern societies. Digitisation and globalisation are creating completely new opportunities and risks, a context in which facts (can) play an enormously important part if they are produced with a quality that makes them credible and purpose-specific. In order for this to actually happen, official statistics must continue to actively pursue the modernisation of their working methods.

This book is not about the technical and methodological challenges associated with digitisation and globalisation; rather, it focuses on statistical sociology, which scientifically deals with the peculiarities and pitfalls of governing-by-numbers, and assigns statistics a suitable position in the future informational ecosystem. Further, the book provides a comprehensive overview of modern issues in official statistics, embodied in a historical and conceptual framework that endows it with different and innovative perspectives. Central to this work is the quality of statistical information provided by official statistics. The implementation of the UN Sustainable Development Goals in the form of indicators is another driving force in the search for answers, and is addressed here….(More)”

Lack of guidance leaves public services in limbo on AI, says watchdog


Dan Sabbagh at the Guardian: “Police forces, hospitals and councils struggle to understand how to use artificial intelligence because of a lack of clear ethical guidance from the government, according to the country’s only surveillance regulator.

The surveillance camera commissioner, Tony Porter, said he received requests for guidance all the time from public bodies which do not know where the limits lie when it comes to the use of facial, biometric and lip-reading technology.

“Facial recognition technology is now being sold as standard in CCTV systems, for example, so hospitals are having to work out if they should use it,” Porter said. “Police are increasingly wearing body cameras. What are the appropriate limits for their use?

“The problem is that there is insufficient guidance for public bodies to know what is appropriate and what is not, and the public have no idea what is going on because there is no real transparency.”

The watchdog’s comments came as it emerged that Downing Street had commissioned a review led by the Committee on Standards in Public Life, whose chairman had called on public bodies to reveal when they use algorithms in decision making.

Lord Evans, a former MI5 chief, told the Sunday Telegraph that “it was very difficult to find out where AI is being used in the public sector” and that “at the very minimum, it should be visible, and declared, where it has the potential for impacting on civil liberties and human rights and freedoms”.

AI is increasingly deployed across the public sector in surveillance and elsewhere. The high court ruled in September that the police use of automatic facial recognition technology to scan people in crowds was lawful.

Its use by South Wales police was challenged by Ed Bridges, a former Lib Dem councillor, who noticed the cameras when he went out to buy a lunchtime sandwich, but the court held that the intrusion into privacy was proportionate….(More)”.

A Matter of Trust: Higher Education Institutions as Information Fiduciaries in an Age of Educational Data Mining and Learning Analytics


Paper by Kyle M. L. Jones, Alan Rubel and Ellen LeClere: “Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data.

We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students….(More)”.

Gendering Smart Mobilities


Book edited by Tanu Priya Uteng, Hilda Rømer Christensen, and Lena Levin: “This book considers gender perspectives on the ‘smart’ turn in urban and transport planning to effectively provide ‘mobility for all’ while simultaneously attending to the goal of creating green and inclusive cities. It deals with the conceptualisation, design, planning, and execution of the fast-emerging ‘smart’ solutions.

The volume questions the efficacy of transformations being brought by smart solutions and highlights the need for a more robust problem formulation to guide the design of smart solutions, and further maps out the need for stronger governance to manage the introduction and proliferation of smart technologies. Authors from a range of disciplinary backgrounds have contributed to this book, designed to converse with mobility studies, transport studies, urban-transport planning, engineering, human geography, sociology, gender studies, and other related fields.

The book fills a substantive gap in the current gender and mobility discourses, and will thus appeal to students and researchers studying mobilities in the social, political, design, technical, and environmental sciences….(More)”.

Biased Algorithms Are Easier to Fix Than Biased People


Sendhil Mullainathan in The New York Times: “In one study published 15 years ago, two people applied for a job. Their résumés were about as similar as two résumés can be. One person was named Jamal, the other Brendan.

In a study published this year, two patients sought medical care. Both were grappling with diabetes and high blood pressure. One patient was black, the other was white.

Both studies documented racial injustice: In the first, the applicant with a black-sounding name got fewer job interviews. In the second, the black patient received worse care.

But they differed in one crucial respect. In the first, hiring managers made biased decisions. In the second, the culprit was a computer program.

As a co-author of both studies, I see them as a lesson in contrasts. Side by side, they show the stark differences between two types of bias: human and algorithmic.

Marianne Bertrand, an economist at the University of Chicago, and I conducted the first study: We responded to actual job listings with fictitious résumés, half of which were randomly assigned a distinctively black name.

The study was: “Are Emily and Greg more employable than Lakisha and Jamal?”

The answer: Yes, and by a lot. Simply having a white name increased callbacks for job interviews by 50 percent.

I published the other study in the journal “Science” in late October with my co-authors: Ziad Obermeyer, a professor of health policy at University of California at Berkeley; Brian Powers, a clinical fellow at Brigham and Women’s Hospital; and Christine Vogeli, a professor of medicine at Harvard Medical School. We focused on an algorithm that is widely used in allocating health care services, and has affected roughly a hundred million people in the United States.

To better target care and provide help, health care systems are turning to voluminous data and elaborately constructed algorithms to identify the sickest patients.

We found these algorithms have a built-in racial bias. At similar levels of sickness, black patients were deemed to be at lower risk than white patients. The magnitude of the distortion was immense: Eliminating the algorithmic bias would more than double the number of black patients who would receive extra help. The problem lay in a subtle engineering choice: to measure “sickness,” they used the most readily available data, health care expenditures. But because society spends less on black patients than equally sick white ones, the algorithm understated the black patients’ true needs.

One difference between these studies is the work needed to uncover bias…(More)”.

The Digital Prism: Transparency and Managed Visibilities in a Datafied World


Book by Mikkel Flyverbom: “We live in times of transparency. Digital technologies expose everything we do, like, and search for, and it is difficult to remain private and out of sight. Meanwhile, many people are concerned about the unchecked powers of tech giants and the hidden operations of big data, artificial intelligence and algorithms and call for more openness and insight. How do we – as individuals, companies and societies – deal with these technological and social transformations? Seen through the prism of digital technologies and data, our lives take new shapes and we are forced to manage our visibilities carefully. This book challenges common ways of thinking about transparency, and argues that the management of visibilities is a crucial, but overlooked force that influences how people live, how organizations work, and how societies and politics operate in a digital, datafied world….(More)”.

One Nation Tracked: An investigation into the smartphone tracking industry


Stuart A. Thompson and Charlie Warzel at the New York Times: “…For brands, following someone’s precise movements is key to understanding the “customer journey” — every step of the process from seeing an ad to buying a product. It’s the Holy Grail of advertising, one marketer said, the complete picture that connects all of our interests and online activity with our real-world actions.

Pointillist location data also has some clear benefits to society. Researchers can use the raw data to provide key insights for transportation studies and government planners. The City Council of Portland, Ore., unanimously approved a deal to study traffic and transit by monitoring millions of cellphones. Unicef announced a plan to use aggregated mobile location data to study epidemics, natural disasters and demographics.

For individual consumers, the value of constant tracking is less tangible. And the lack of transparency from the advertising and tech industries raises still more concerns.

Does a coupon app need to sell second-by-second location data to other companies to be profitable? Does that really justify allowing companies to track millions and potentially expose our private lives?

Data companies say users consent to tracking when they agree to share their location. But those consent screens rarely make clear how the data is being packaged and sold. If companies were clearer about what they were doing with the data, would anyone agree to share it?

What about data collected years ago, before hacks and leaks made privacy a forefront issue? Should it still be used, or should it be deleted for good?

If it’s possible that data stored securely today can easily be hacked, leaked or stolen, is this kind of data worth that risk?

Is all of this surveillance and risk worth it merely so that we can be served slightly more relevant ads? Or so that hedge fund managers can get richer?

The companies profiting from our every move can’t be expected to voluntarily limit their practices. Congress has to step in to protect Americans’ needs as consumers and rights as citizens.

Until then, one thing is certain: We are living in the world’s most advanced surveillance system. This system wasn’t created deliberately. It was built through the interplay of technological advance and the profit motive. It was built to make money. The greatest trick technology companies ever played was persuading society to surveil itself….(More)”.

Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century


Paper by Jean-Claude Burgelman et al: “Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective….(More)”.

Accelerating Medicines Partnership (AMP): Improving Drug Research Efficiency through Biomarker Data Sharing


Data Collaborative Case Study by Michelle Winowatan, Andrew Young, and Stefaan Verhulst: “Accelerating Medicines Partnership (AMP) is a cross-sector data-sharing partnership in the United States between the National Institutes of Health (NIH), the Food and Drug Administration (FDA), multiple biopharmaceutical and life science companies, as well as non-profit organizations that seeks to improve the efficiency of developing new diagnostics and treatments for several types of disease. To achieve this goal, the partnership created a pre-competitive collaborative ecosystem where the biomedical community can pool data and resources that are relevant to the prioritized disease areas. A key component of the partnership is to make biomarkers data available to the medical research community through online portals.

Data Collaboratives Model: Based on our typology of data collaborative models, AMP is an example of the data pooling model of data collaboration, specifically a public data pool. Public data pools co-mingle data assets from multiple data holders — in this case pharmaceutical companies — and make those shared assets available on the web. Pools often limit contributions to approved partners (as public data pools are not crowdsourcing efforts), but access to the shared assets is open, enabling independent re-uses.

Data Stewardship Approach: Data stewardship is built into the partnership through the establishment of an executive committee, which governs the entire partnership, and a steering committee for each disease area, which governs each of the sub-projects within AMP. These committees consist of representatives from the institutional partners involved in AMP and perform data stewards function including enabling inter-institutional engagement as well as intra-institutional coordination, data audit and assessment of value and risk, communication of findings, and nurture the collaboration to sustainability….(Full Case Study)”.

Open data for electricity modeling: Legal aspects


Paper by Lion Hirth: “Power system modeling is data intensive. In Europe, electricity system data is often available from sources such as statistical offices or system operators. However, it is often unclear if these data can be legally used for modeling, and in particular if such use infringes intellectual property rights. This article reviews the legal status of power system data, both as a guide for data users and for data publishers.

It is based on interpretation of the law, a review of the secondary literature, an analysis of the licenses used by major data distributors, expert interviews, and a series of workshops. A core finding is that in many cases the legality of current practices is doubtful: in fact, it seems likely that modelers infringe intellectual property rights quite regularly. This is true for industry analysis but also academic researchers. A straightforward solution is open data – the idea that data can be freely used, modified, and shared by anyone for any purpose. To be open, it is not sufficient for data to be accessible free of cost, it must also come with an open data license, the most common types of which are also reviewed in this paper….(More)”.