What if you ask and they say yes? Consumers' willingness to disclose personal data is stronger than you think

Curated on Posted on by Stefaan Verhulst

Grzegorz Mazurek and Karolina Małagocka at Business Horizons: “Technological progress—including the development of online channels and universal access to the internet via mobile devices—has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage.

This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies’ success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts—which, combined, constitute a main T (i.e., the transfer of personal data)—deserve attention when seeking customer information that can be converted to competitive advantage and market success….(More)”.

10 Privacy Risks and 10 Privacy Enhancing Technologies to Watch in the Next Decade

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Future of Privacy Forum: “Today, FPF is publishing a white paper co-authored by CEO Jules Polonetsky and hackylawyER Founder Elizabeth Renieris to help corporate officers, nonprofit leaders, and policymakers better understand privacy risks that will grow in prominence during the 2020s, as well as rising technologies that will be used to help manage privacy through the decade. Leaders must understand the basics of technologies like biometric scanning, collaborative robotics, and spatial computing in order to assess how existing and proposed policies, systems, and laws will address them, and to support appropriate guidance for the implementation of new digital products and services.

The white paper, Privacy 2020: 10 Privacy Risks and 10 Privacy Enhancing Technologies to Watch in the Next Decade, identifies ten technologies that are likely to create increasingly complex data protection challenges. Over the next decade, privacy considerations will be driven by innovations in tech linked to human bodies, health, and social networks; infrastructure; and computing power. The white paper also highlights ten developments that can enhance privacy – providing cause for optimism that organizations will be able to manage data responsibly. Some of these technologies are already in general use, some will soon be widely deployed, and others are nascent….(More)”.

The Gray Spectrum: Ethical Decision Making with Geospatial and Open Source Analysis

Curated on Posted on by Stefaan Verhulst

Report by The Stanley Center for Peace and Security: “Geospatial and open source analysts face decisions in their work that can directly or indirectly cause harm to individuals, organizations, institutions, and society. Though analysts may try to do the right thing, such ethically-informed decisions can be complex. This is particularly true for analysts working on issues related to nuclear nonproliferation or international security, analysts whose decisions on whether to publish certain findings could have far-reaching consequences.

The Stanley Center for Peace and Security and the Open Nuclear Network (ONN) program of One Earth Future Foundation convened a workshop to explore these ethical challenges, identify resources, and consider options for enhancing the ethical practices of geospatial and open source analysis communities.

This Readout & Recommendations brings forward observations from that workshop. It describes ethical challenges that stakeholders from relevant communities face. It concludes with a list of needs participants identified, along with possible strategies for promoting sustaining behaviors that could enhance the ethical conduct of the community of nonproliferation analysts working with geospatial and open source data.

Some Key Findings

  • A code of ethics could serve important functions for the community, including giving moral guidance to practitioners, enhancing public trust in their work, and deterring unethical behavior. Participants in the workshop saw a significant value in such a code and offered ideas for developing one.
  • Awareness of ethical dilemmas and strong ethical reasoning skills are essential for sustaining ethical practices, yet professionals in this field might not have easy access to such training. Several approaches could improve ethics education for the field overall, including starting a body of literature, developing model curricula, and offering training for students and professionals.
  • Other stakeholders—governments, commercial providers, funders, organizations, management teams, etc.—should contribute to the discussion on ethics in the community and reinforce sustaining behaviors….(More)”.

You Are Now Remotely Controlled

Curated on Posted on by Stefaan Verhulst

Essay by Shoshana Zuboff in The New York Times: “…Only repeated crises have taught us that these platforms are not bulletin boards but hyper-velocity global bloodstreams into which anyone may introduce a dangerous virus without a vaccine. This is how Facebook’s chief executive, Mark Zuckerberg, could legally refuse to remove a faked video of Speaker of the House Nancy Pelosi and later double down on this decision, announcing that political advertising would not be subject to fact-checking.

All of these delusions rest on the most treacherous hallucination of them all: the belief that privacy is private. We have imagined that we can choose our degree of privacy with an individual calculation in which a bit of personal information is traded for valued services — a reasonable quid pro quo.For example, when Delta Air Lines piloted a biometric data system at the Atlanta airport, the company reported that of nearly 25,000 customers who traveled there each week, 98 percent opted into the process, noting that “the facial recognition option is saving an average of two seconds for each customer at boarding, or nine minutes when boarding a wide body aircraft.”

In fact the rapid development of facial recognition systems reveals the public consequences of this supposedly private choice. Surveillance capitalists have demanded the right to take our faces wherever they appear — on a city street or a Facebook page. The Financial Times reported that a Microsoft facial recognition training database of 10 million images plucked from the internet without anyone’s knowledge and supposedly limited to academic research was employed by companies like IBM and state agencies that included the United States and Chinese military. Among these were two Chinese suppliers of equipment to officials in Xinjiang, where members of the Uighur community live in open-air prisons under perpetual surveillance by facial recognition systems.

Privacy is not private, because the effectiveness of these and other private or public surveillance and control systems depends upon the pieces of ourselves that we give up — or that are secretly stolen from us.

Our digital century was to have been democracy’s Golden Age. Instead, we enter its third decade marked by a stark new form of social inequality best understood as “epistemic inequality.” It recalls a pre-Gutenberg era of extreme asymmetries of knowledge and the power that accrues to such knowledge, as the tech giants seize control of information and learning itself. The delusion of “privacy as private” was crafted to breed and feed this unanticipated social divide. Surveillance capitalists exploit the widening inequity of knowledge for the sake of profits. They manipulate the economy, our society and even our lives with impunity, endangering not just individual privacy but democracy itself. Distracted by our delusions, we failed to notice this bloodless coup from above….(More)”.

Paging Dr. Google: How the Tech Giant Is Laying Claim to Health Data

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Wall Street Journal: “Roughly a year ago, Google offered health-data company Cerner Corp.an unusually rich proposal.

Cerner was interviewing Silicon Valley giants to pick a storage provider for 250 million health records, one of the largest collections of U.S. patient data. Google dispatched former chief executive Eric Schmidt to personally pitch Cerner over several phone calls and offered around $250 million in discounts and incentives, people familiar with the matter say. 

Google had a bigger goal in pushing for the deal than dollars and cents: a way to expand its effort to collect, analyze and aggregate health data on millions of Americans. Google representatives were vague in answering questions about how Cerner’s data would be used, making the health-care company’s executives wary, the people say. Eventually, Cerner struck a storage deal with Amazon.com Inc. instead.

The failed Cerner deal reveals an emerging challenge to Google’s move into health care: gaining the trust of health care partners and the public. So far, that has hardly slowed the search giant.

Google has struck partnerships with some of the country’s largest hospital systems and most-renowned health-care providers, many of them vast in scope and few of their details previously reported. In just a few years, the company has achieved the ability to view or analyze tens of millions of patient health records in at least three-quarters of U.S. states, according to a Wall Street Journal analysis of contractual agreements. 

In certain instances, the deals allow Google to access personally identifiable health information without the knowledge of patients or doctors. The company can review complete health records, including names, dates of birth, medications and other ailments, according to people familiar with the deals.

The prospect of tech giants’ amassing huge troves of health records has raised concerns among lawmakers, patients and doctors, who fear such intimate data could be used without individuals’ knowledge or permission, or in ways they might not anticipate. 

Google is developing a search tool, similar to its flagship search engine, in which patient information is stored, collated and analyzed by the company’s engineers, on its own servers. The portal is designed for use by doctors and nurses, and eventually perhaps patients themselves, though some Google staffers would have access sooner. 

Google executives and some health systems say that detailed data sharing has the potential to improve health outcomes. Large troves of data help fuel algorithms Google is creating to detect lung cancer, eye disease and kidney injuries. Hospital executives have long sought better electronic record systems to reduce error rates and cut down on paperwork….

Legally, the information gathered by Google can be used for purposes beyond diagnosing illnesses, under laws enacted during the dial-up era. U.S. federal privacy laws make it possible for health-care providers, with little or no input from patients, to share data with certain outside companies. That applies to partners, like Google, with significant presences outside health care. The company says its intentions in health are unconnected with its advertising business, which depends largely on data it has collected on users of its many services, including email and maps.

Medical information is perhaps the last bounty of personal data yet to be scooped up by technology companies. The health data-gathering efforts of other tech giants such as Amazon and International Business Machines Corp. face skepticism from physician and patient advocates. But Google’s push in particular has set off alarm bells in the industry, including over privacy concerns. U.S. senators, as well as health-industry executives, are questioning Google’s expansion and its potential for commercializing personal data….(More)”.

Trusted smart statistics: Motivations and principles

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Paper by Fabio Ricciato et al : “In this contribution we outline the concept of Trusted Smart Statistics as the natural evolution of official statistics in the new datafied world. Traditional data sources, namely survey and administrative data, represent nowadays a valuable but small portion of the global data stock, much thereof being held in the private sector. The availability of new data sources is only one aspect of the global change that concerns official statistics. Other aspects, more subtle but not less important, include the changes in perceptions, expectations, behaviours and relations between the stakeholders. The environment around official statistics has changed: statistical offices are not any more data monopolists, but one prominent species among many others in a larger (and complex) ecosystem. What was established in the traditional world of legacy data sources (in terms of regulations, technologies, practices, etc.) is not guaranteed to be sufficient any more with new data sources.

Trusted Smart Statistics is not about replacing existing sources and processes, but augmenting them with new ones. Such augmentation however will not be only incremental: the path towards Trusted Smart Statistics is not about tweaking some components of the legacy system but about building an entirely new system that will coexist with the legacy one. In this position paper we outline some key design principles for the new Trusted Smart Statistics system. Taken collectively they picture a system where the smart and trust aspects enable and reinforce each other. A system that is more extrovert towards external stakeholders (citizens, private companies, public authorities) with whom Statistical Offices will be sharing computation, control, code, logs and of course final statistics, without necessarily sharing the raw input data….(More)”.

Towards adaptive governance in big data health research: implementing regulatory principles

Curated on Posted on by Stefaan Verhulst

Chapter by Alessandro Blasimme and Effy Vayena: “While data-enabled health care systems are in their infancy, biomedical research is rapidly adopting the big data paradigm. Digital epidemiology for example, already employs data generated outside the public health care system – that is, data generated without the intent of using them for epidemiological research – to understand and prevent patterns of diseases in populations (Salathé 2018)(Salathé 2018). Precision medicine – pooling together genomic, environmental and lifestyle data – also represents a prominent example of how data integration can drive both fundamental and translational research in important medical domains such as oncology (D. C. Collins et al. 2017). All of this requires the collection, storage, analysis and distribution of massive amounts of personal information as well as the use of state-of-the art data analytics tools to uncover healthand disease related patterns.


The realization of the potential of big data in health evokes a necessary commitment to a sense of “continuity” articulated in three distinct ways: a) from data generation to use (as in the data enabled learning health care ); b) from research to clinical practice e.g. discovery of new mutations in the context of diagnostics; c) from strictly speaking health data (Vayena and Gasser 2016) e.g. clinical records, to less so e.g. tweets used in digital epidemiology. These continuities face the challenge of regulatory and governance approaches that were designed for clear data taxonomies, for a less blurred boundary between research and clinical practice, and for rules that focused mostly on data generation and less on their eventual and multiple uses.

The result is significant uncertainty about how responsible use of such large amounts of sensitive personal data could be fostered. In this chapter we focus on the uncertainties surrounding the use of biomedical big data in the context of health research. Are new criteria needed to review biomedical big data research projects? Do current mechanisms, such as informed consent, offer sufficient protection to research participants’ autonomy and privacy in this new context? Do existing oversight mechanisms ensure transparency and accountability in data access and sharing? What monitoring tools are available to assess how personal data are used over time? Is the equitable distribution of benefits accruing from such data uses considered, or can it be ensured? How is the public being involved – if at all – with decisions about creating and using large data
repositories for research purposes? What is the role that IT (information technology) players, and especially big ones, acquire in research? And what regulatory instruments do we have to ensure that such players do not undermine the independence of research?…(More)”.

Responsible data sharing in a big data-driven translational research platform: lessons learned

Curated on Posted on by Stefaan Verhulst

Paper by S. Kalkman et al: “The sharing of clinical research data is increasingly viewed as a moral duty [1]. Particularly in the context of making clinical trial data widely available, editors of international medical journals have labeled data sharing a highly efficient way to advance scientific knowledge [2,3,4]. The combination of even larger datasets into so-called “Big Data” is considered to offer even greater benefits for science, medicine and society [5]. Several international consortia have now promised to build grand-scale, Big Data-driven translational research platforms to generate better scientific evidence regarding disease etiology, diagnosis, treatment and prognosis across various disease areas [6,7,8].

Despite anticipated benefits, large-scale sharing of health data is charged with ethical questions. Stakeholders have been urged to consider how to manage privacy and confidentiality issues, ensure valid informed consent, and determine who gets to decide about data access [9]. More fundamentally, new data sharing activities prompt questions about social justice and public trust [10]. To balance potential benefits and ethical considerations, data sharing platforms require guidance for the processes of interaction and decision-making. In the European Union (EU), legal norms specified for the sharing of personal data for health research, most notably those set out in the General Data Protection Regulation (GDPR) (EU 2016/679), remain open to interpretation and offer limited practical guidance to researchers [12,12,13]. Striking in this regard is that the GDPR itself stresses the importance of adherence to ethical standards, when broad consent is put forward as a legal basis for the processing of personal data. For example, Recital 33 of the GDPR states that data subjects should be allowed to give “consent to certain areas of scientific research when in keeping with recognised ethical standards for scientific research” [14]. In fact, the GDPR actually encourages data controllers to establish self-regulating mechanisms, such as a code of conduct. To foster responsible and sustainable data sharing in translational research platforms, ethical guidance and governance is therefore necessary. Here, we define governance as ‘the processes of interaction and decision-making among the different stakeholders that are involved in a collective problem that lead to the creation, reinforcement, or reproduction of social norms and institutions’…(More)”.

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

Curated on Posted on by Stefaan Verhulst

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)”.

Biased Algorithms Are Easier to Fix Than Biased People

Curated on Posted on by Stefaan Verhulst

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)”.