Stefaan Verhulst

Press Release: “The Council of Europe has issued a set of guidelines to its 47 member states urging them to ensure, in law and practice, that the processing of health-related data is done in full respect of human rights, notably the right to privacy and data protection.

With the development of new technological tools in the health sector the volume of health-related data processed has grown exponentially showing the need for guidance for health administrations and professionals.

In a Recommendation, applicable to both the public and private sectors, the Council of Europe´s Committee of Ministers, calls on governments to transmit these guidelines to health-care systems and to actors processing health-related data, in particular health-care professionals and data protection officers.

The recommendation contains a set of principles to protect health-related data incorporating the novelties introduced in the updated Council of Europe data protection convention, known as “Convention 108+”, opened for signature in October 2018.

The Committee of Ministers underlines that health-related data should be protected by appropriate security measures taking into account the latest technological developments, their sensitive nature and the assessment of potential risks. Protection measures should be incorporated by design to any information system which processes health-related data.

The recommendation contains guidance with regard to various issues including the legitimate basis for the data processing of health-care data – notably consent by the data subject -, data concerning unborn children, health-related genetic data, the sharing of health-related data by professionals and the storage of data.

The guidelines list a number of rights of data subjects, crucially the transparency of data processing. They also contain a number of principles that should be respected when data are processed for scientific research, when they are collected by mobile devices or when they are transferred across borders….(More)”.

Leo Gringut at the International Policy Digest: “The philosophy behind “Open Data for All” turns on the idea that easy access to government data offers everyday New Yorkers the chance to grow and innovate: “Data is more than just numbers – it’s information that can create new opportunities and level the playing field for New Yorkers. It’s the illumination that changes frameworks, the insight that turns impenetrable issues into solvable problems.” Fundamentally, the newfound accessibility of City data is revolutionizing NYC business. According to Albert Webber, Program Manager for Open Data, City of New York, a key part of his job is “to engage the civic technology community that we have, which is very strong, very powerful in New York City.”

Fundamentally, Open Data is a game-changer for hundreds of New York companies, from startups to corporate giants, all of whom rely on data for their operations. The effect is set to be particularly profound in New York City’s most important economic sector: real estate. Seeking to transform the real estate and construction market in the City, valued at a record-setting $1 trillion in 2016, companies have been racing to develop tools that will harness the power of Open Data to streamline bureaucracy and management processes.

One such technology is the Citiscape app. Developed by a passionate team of real estate experts with more than 15 years of experience in the field, the app assembles data from the Department of Building and the Environmental Control Board into one easy-to-navigate interface. According to Citiscape Chief Operational Officer Olga Khaykina, the secret is in the app’s simplicity, which puts every aspect of project management at the user’s fingertips. “We made DOB and ECB just one tap away,” said Khaykina. “You’re one tap away from instant and accurate updates and alerts from the DOB that will keep you informed about any changes to ongoing project. One tap away from organized and cloud-saved projects, including accessible and coordinated interaction with all team members through our in-app messenger. And one tap away from uncovering technical information about any building in NYC, just by entering its address.” Gone are the days of continuously refreshing the DOB website in hopes of an update on a minor complaint or a status change regarding your project; Citiscape does the busywork so you can focus on your project.

The Citiscape team emphasized that, without access to Open Data, this project would have been impossible….(More)”.

Book edited by Alison Tonkin and Julia Whitaker: “The role of play in human and animal development is well established, and its educational and therapeutic value is widely supported in the literature. This innovative book extends the play debate by assembling and examining the many pieces of the play puzzle from the perspective of public health. It tackles the dual aspects of art and science which inform both play theory and public health policy, and advocates for a ‘playful’ pursuit of public health, through the integration of evidence from parallel scientific and creative endeavors.

Drawing on international research evidence, the book addresses some of the major public health concerns of the 21st century – obesity, inactivity, loneliness and mental health – advocating for creative solutions to social disparities in health and wellbeing. From attachment at the start of life to detachment at life’s ending, in the home and in the workplace, and across virtual and physical environments, play is presented as vital to the creation of a new ‘culture of health’.

This book represents a valuable resource for students, academics, practitioners and policy-makers across a range of fields of interest including play, health, the creative arts and digital and environmental design….(More)”.

Nick Brown at Reuters: “The U.S. Census Bureau has asked tech giants Google, Facebook and Twitter to help it fend off “fake news” campaigns it fears could disrupt the upcoming 2020 count, according to Census officials and multiple sources briefed on the matter.

The push, the details of which have not been previously reported, follows warnings from data and cybersecurity experts dating back to 2016 that right-wing groups and foreign actors may borrow the “fake news” playbook from the last presidential election to dissuade immigrants from participating in the decennial count, the officials and sources told Reuters.

The sources, who asked not to be named, said evidence included increasing chatter on platforms like “4chan” by domestic and foreign networks keen to undermine the survey. The census, they said, is a powerful target because it shapes U.S. election districts and the allocation of more than $800 billion a year in federal spending.

Ron Jarmin, the Deputy Director of the Census Bureau, confirmed the bureau was anticipating disinformation campaigns, and was enlisting the help of big tech companies to fend off the threat.

“We expect that (the census) will be a target for those sorts of efforts in 2020,” he said.

Census Bureau officials have held multiple meetings with tech companies since 2017 to discuss ways they could help, including as recently as last week, Jarmin said.

So far, the bureau has gotten initial commitments from Alphabet Inc’s Google, Twitter Inc and Facebook Inc to help quash disinformation campaigns online, according to documents summarizing some of those meetings reviewed by Reuters.

But neither Census nor the companies have said how advanced any of the efforts are….(More)”.

Book edited by the Information Resources Management Association: “Businesses are looking for methods to incorporate social entrepreneurship in order to generate a positive return to society. Social enterprises have the ability to improve societies through altruistic work to create sustainable work environments for future entrepreneurs and their communities.

Social Entrepreneurship: Concepts, Methodologies, Tools, and Applications is a useful scholarly resource that examines the broad topic of social entrepreneurship by looking at relevant theoretical frameworks and fundamental terms. It also addresses the challenges and solutions social entrepreneurs face as they address their corporate social responsibility in an effort to redefine the goals of today’s enterprises and enhance the potential for growth and change in every community. Highlighting a range of topics such as the social economy, corporate social responsibility, and competitive advantage, this multi-volume book is ideally designed for business professionals, entrepreneurs, start-up companies, academics, and graduate-level students in the fields of economics, business administration, sociology, education, politics, and international relations….(More)”.

Paper by Yoshio Kamijo et al: “People to be born in the future have no direct
influence on current affairs. Given the disconnect between people who are currently living and those who will inherit the planet left for them, individuals who are currently alive tend to be more oriented toward the present, posing a fundamental problem related to sustainability.

In this study, we propose a new framework for reconciling the disconnect between the present and the future whereby some individuals in the current generation serve as an imaginary future generation that negotiates with individuals in the real-world present. Through a laboratory-controlled intergenerational sustainability dilemma game (ISDG), we show how the presence of negotiators for a future generation increases the benefits of future generations. More specifically, we found that when faced with members of an imaginary future generation, 60% of participants selected
an option that promoted sustainability. In contrast, when the imaginary future generation was not salient, only 28% of participants chose the sustainable option…(More)”.

Colin Wood at StateScoop: “Civilian analysts and officers within the New York City Police Department are using a unique computational tool to spot patterns in crime data that is closing cases.

A collection of machine-learning models, which the department calls Patternizr, was first deployed in December 2016, but the department only revealed the system last month when its developers published a research paper in the Informs Journal on Applied Analytics. Drawing on 10 years of historical data about burglary, robbery and grand larceny, the tool is the first of its kind to be used by law enforcement, the developers wrote.

The NYPD hired 100 civilian analysts in 2017 to use Patternizr. It’s also available to all officers through the department’s Domain Awareness System, a citywide network of sensors, databases, devices, software and other technical infrastructure. Researchers told StateScoop the tool has generated leads on several cases that traditionally would have stretched officers’ memories and traditional evidence-gathering abilities.

Connecting similar crimes into patterns is a crucial part of gathering evidence and eventually closing in on an arrest, said Evan Levine, the NYPD’s assistant commissioner of data analytics and one of Patternizr’s developers. Taken independently, each crime in a string of crimes may not yield enough evidence to identify a perpetrator, but the work of finding patterns is slow and each officer only has a limited amount of working knowledge surrounding an incident, he said.

“The goal here is to alleviate all that kind of busywork you might have to do to find hits on a pattern,” said Alex Chohlas-Wood, a Patternizr researcher and deputy director of the Computational Policy Lab at Stanford University.

The knowledge of individual officers is limited in scope by dint of the NYPD’s organizational structure. The department divides New York into 77 precincts, and a person who commits crimes across precincts, which often have arbitrary boundaries, is often more difficult to catch because individual beat officers are typically focused on a single neighborhood.

There’s also a lot of data to sift through. In 2016 alone, about 13,000 burglaries, 15,000 robberies and 44,000 grand larcenies were reported across the five boroughs.

Levine said that last month, police used Patternizr to spot a pattern of three knife-point robberies around a Bronx subway station. It would have taken police much longer to connect those crimes manually, Levine said.

The software works by an analyst feeding it “seed” case, which is then compared against a database of hundreds of thousands of crime records that Patternizr has already processed. The tool generates a “similarity score” and returns a rank-ordered list and a map. Analysts can read a few details of each complaint before examining the seed complaint and similar complaints in a detailed side-by-side view or filtering results….(More)”.

Paper by Crystal G. Konny, Brendan K. Williams, and David M. Friedman: “The Bureau of Labor Statistics (BLS) has generally relied on its own sample surveys to collect the price and expenditure information necessary to produce the Consumer Price Index (CPI). The burgeoning availability of big data has created a proliferation of information that could lead to methodological improvements and cost savings in the CPI. The BLS has undertaken several pilot projects in an attempt to supplement and/or replace its traditional field collection of price data with alternative sources. In addition to cost reductions, these projects have demonstrated the potential to expand sample size, reduce respondent burden, obtain transaction prices more consistently, and improve price index estimation by incorporating real-time expenditure information—a foundational component of price index theory that has not been practical until now. In CPI, we use the term alternative data to refer to any data not collected through traditional field collection procedures by CPI staff, including third party datasets, corporate data, and data collected through web scraping or retailer API’s. We review how the CPI program is adapting to work with alternative data, followed by discussion of the three main sources of alternative data under consideration by the CPI with a description of research and other steps taken to date for each source. We conclude with some words about future plans… (More)”.

Paper by Erik Brynjolfsson, Avinash Collis, and Felix Eggers: “Gross domestic product (GDP) and derived metrics such as productivity have been central to our understanding of economic progress and well-being. In principle, changes in consumer surplus provide a superior, and more direct, measure of changes in well-being, especially for digital goods. In practice, these alternatives have been difficult to quantify. We explore the potential of massive online choice experiments to measure consumer surplus. We illustrate this technique via several empirical examples which quantify the valuations of popular digital goods and categories. Our examples include incentive-compatible discrete-choice experiments where online and laboratory participants receive monetary compensation if and only if they forgo goods for predefined periods.

For example, the median user needed a compensation of about $48 to forgo Facebook for 1 mo. Our overall analyses reveal that digital goods have created large gains in well-being that are not reflected in conventional measures of GDP and productivity. By periodically querying a large, representative sample of goods and services, including those which are not priced in existing markets, changes in consumer surplus and other new measures of well-being derived from these online choice experiments have the potential for providing cost-effective supplements to the existing national income and product accounts….(More)”.

Blog Post by Annette Zimmermann and Bendert Zevenbergen: “… In what follows, we outline seven ‘AI ethics traps’. In doing so, we hope to provide a resource for readers who want to understand and navigate the public debate on the ethics of AI better, who want to contribute to ongoing discussions in an informed and nuanced way, and who want to think critically and constructively about ethical considerations in science and technology more broadly. Of course, not everybody who contributes to the current debate on AI Ethics is guilty of endorsing any or all of these traps: the traps articulate extreme versions of a range of possible misconceptions, formulated in a deliberately strong way to highlight the ways in which one might prematurely dismiss ethical reasoning about AI as futile.

1. The reductionism trap:

“Doing the morally right thing is essentially the same as acting in a fair way. (or: transparent, or egalitarian, or <substitute any other value>). So ethics is the same as fairness (or transparency, or equality, etc.). If we’re being fair, then we’re being ethical.”

            Even though the problem of algorithmic bias and its unfair impact on decision outcomes is an urgent problem, it does not exhaust the ethical problem space. As important as algorithmic fairness is, it is crucial to avoid reducing ethics to a fairness problem alone. Instead, it is important to pay attention to how the ethically valuable goal of optimizing for a specific value like fairness interacts with other important ethical goals. Such goals could include—amongst many others—the goal of creating transparent and explainable systems which are open to democratic oversight and contestation, the goal of improving the predictive accuracy of machine learning systems, the goal of avoiding paternalistic infringements of autonomy rights, or the goal of protecting the privacy interests of data subjects. Sometimes, these different values may conflict: we cannot always optimize for everything at once. This makes it all the more important to adopt a sufficiently rich, pluralistic view of the full range of relevant ethical values at stake—only then can one reflect critically on what kinds of ethical trade-offs one may have to confront.

2. The simplicity trap:

“In order to make ethics practical and action-guiding, we need to distill our moral framework into a user-friendly compliance checklist. After we’ve decided on a particular path of action, we’ll go through that checklist to make sure that we’re being ethical.”

            Given the high visibility and urgency of ethical dilemmas arising in the context of AI, it is not surprising that there are more and more calls to develop actionable AI ethics checklists. For instance, a 2018 draft report by the European Commission’s High-Level Expert Group on Artificial Intelligence specifies a preliminary ‘assessment list’ for ‘trustworthy AI’. While the report plausibly acknowledges that such an assessment list must be context-sensitive and that it is not exhaustive, it nevertheless identifies a list of ten fixed ethical goals, including privacy and transparency. But can and should ethical values be articulated in a checklist in the first place? It is worth examining this underlying assumption critically. After all, a checklist implies a one-off review process: on that view, developers or policy-makers could determine whether a particular system is ethically defensible at a specific moment in time, and then move on without confronting any further ethical concerns once the checklist criteria have been satisfied once. But ethical reasoning cannot be a static one-off assessment: it required an ongoing process of reflection, deliberation, and contestation. Simplicity is good—but the willingness to reconsider simple frameworks, when required, is better. Setting a fixed ethical agenda ahead of time risks obscuring new ethical problems that may arise at a later point in time, or ongoing ethical problems that become apparent to human decision-makers only later.

3. The relativism trap:

“We all disagree about what is morally valuable, so it’s pointless to imagine that there is a universalbaseline against which we can use in order to evaluate moral choices. Nothing is objectively morally good: things can only be morally good relative to each person’s individual value framework.”

            Public discourse on the ethics of AI frequently produces little more than an exchange of personal opinions or institutional positions. In light of pervasive moral disagreement, it is easy to conclude that ethical reasoning can never stand on firm ground: it always seems to be relative to a person’s views and context. But this does not mean that ethical reasoning about AI and its social and political implications is futile: some ethical arguments about AI may ultimately be more persuasive than others. While it may not always be possible to determine ‘the one right answer’, it is often possible to identify at least  some paths of action are clearly wrong, and some paths of action that are comparatively better (if not optimal all things considered). If that is the case, comparing the respective merits of ethical arguments can be action-guiding for developers and policy-makers, despite the presence of moral disagreement. Thus, it is possible and indeed constructive for AI ethics to welcome value pluralism, without collapsing into extreme value relativism.

4. The value alignment trap:

“If relativism is wrong (see #3), there must be one morally right answer. We need to find that right answer, and ensure that everyone in our organisation acts in alignment with that answer. If our ethical reasoning leads to moral disagreement, that means that we have failed.”…(More)”.