Chapter by Roberto da Mota Ueti, Daniela Fernandez Espinosa, Laura Rafferty, Patrick C. K. Hung in Big Data Applications and Use Cases: “Big Data is changing our world with masses of information stored in huge servers spread across the planet. This new technology is changing not only companies but governments as well. Mexico and Brazil, two of the most influential countries in Latin America, are entering a new era and as a result, facing challenges in all aspects of public policy. Using Big Data, the Brazilian Government is trying to decrease spending and use public money better by grouping public information with stored information on citizens in public services. With new reforms in education, finances and telecommunications, the Mexican Government is taking on a bigger role in efforts to channel the country’s economic policy into an improvement of the quality of life of their habitants. It is known that technology is an important part for sub-developed countries, who are trying to make a difference in certain contexts such as reducing inequality or regulating the good usage of economic resources. The good use of Big Data, a new technology that is in charge of managing a big quantity of information, can be crucial for the Mexican Government to reach the goals that have been set in the past under Peña Nieto’s administration. This article focuses on how the Brazilian and Mexican Governments are managing the emerging technologies of Big Data and how it includes them in social and industrial projects to enhance the growth of their economies. The article also discusses the benefits of these uses of Big Data and the possible problems that occur related to security and privacy of information….(More)’
Big data: big power shifts?
Special issue of Internet Policy Review: “Facing general conceptions of the power effects of big data, this thematic edition is interested in studies that scrutinise big data and power in concrete fields of application. It brings together scholars from different disciplines who analyse the fields agriculture, education, border control and consumer policy. As will be made explicit in the following, each of the articles tells us something about firstly, what big data is and how it relates to power. They secondly also shed light on how we should shape “the big data society” and what research questions need to be answered to be able to do so….
The ethics of big data in big agriculture
Isabelle M. Carbonell, University of California, Santa Cruz
Regulating “big data education” in Europe: lessons learned from the US
Yoni Har Carmel, University of Haifa
The borders, they are a-changin’! The emergence of socio-digital borders in the EU
Magdalena König, Maastricht University
Beyond consent: improving data protection through consumer protection law
Michiel Rhoen, Leiden University…
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Nudging – Possibilities, Limitations and Applications in European Law and Economics
Book edited by Mathis, Klaus and Tor, Avishalom: “This anthology provides an in-depth analysis and discusses the issues surrounding nudging and its use in legislation, regulation, and policy making more generally. The 17 essays in this anthology provide startling insights into the multifaceted debate surrounding the use of nudges in European Law and Economics.
Nudging is a tool aimed at altering people’s behaviour in a predictable way without forbidding any option or significantly changing economic incentives. It can be used to help people make better decisions to influence human behaviour without forcing them because they can opt out. Its use has sparked lively debates in academia as well as in the public sphere. This book explores who decides which behaviour is desired. It looks at whether or not the state has sufficient information for debiasing, and if there are clear-cut boundaries between paternalism, manipulation and indoctrination. The first part of this anthology discusses the foundations of nudging theory and the problems associated, as well as outlining possible solutions to the problems raised. The second part is devoted to the wide scope of applications of nudges from contract law, tax law and health claim regulations, among others.
This volume is a result of the flourishing annual Law and Economics Conference held at the law faculty of the University of Lucerne. The conferences have been instrumental in establishing a strong and ever-growing Law and Economics movement in Europe, providing unique insights in the challenges faced by Law and Economics when applied in European legal traditions….(More)”
Reining in the Big Promise of Big Data: Transparency, Inequality, and New Regulatory Frontiers
Paper by Philipp Hacker and Bilyana Petkova: “The growing differentiation of services based on Big Data harbors the potential for both greater societal inequality and for greater equality. Anti-discrimination law and transparency alone, however, cannot do the job of curbing Big Data’s negative externalities while fostering its positive effects.
To rein in Big Data’s potential, we adapt regulatory strategies from behavioral economics, contracts and criminal law theory. Four instruments stand out: First, active choice may be mandated between data collecting services (paid by data) and data free services (paid by money). Our suggestion provides concrete estimates for the price range of a data free option, sheds new light on the monetization of data collecting services, and proposes an “inverse predatory pricing” instrument to limit excessive pricing of the data free option. Second, we propose using the doctrine of unconscionability to prevent contracts that unreasonably favor data collecting companies. Third, we suggest democratizing data collection by regular user surveys and data compliance officers partially elected by users. Finally, we trace back new Big Data personalization techniques to the old Hartian precept of treating like cases alike and different cases – differently. If it is true that a speeding ticket over $50 is less of a disutility for a millionaire than for a welfare recipient, the income and wealth-responsive fines powered by Big Data that we suggest offer a glimpse into the future of the mitigation of economic and legal inequality by personalized law. Throughout these different strategies, we show how salience of data collection can be coupled with attempts to prevent discrimination against and exploitation of users. Finally, we discuss all four proposals in the context of different test cases: social media, student education software and credit and cell phone markets.
Many more examples could and should be discussed. In the face of increasing unease about the asymmetry of power between Big Data collectors and dispersed users, about differential legal treatment, and about the unprecedented dimensions of economic inequality, this paper proposes a new regulatory framework and research agenda to put the powerful engine of Big Data to the benefit of both the individual and societies adhering to basic notions of equality and non-discrimination….(More)”
Open data behind WA hospital waiting times app
Asha Barbaschow at ZDNet: “Patients seeking urgent medical care in Perth can now view emergency waiting times for local hospitals, thanks to a new app developed in Australia.
The app, WA Emergency Waiting Times, uses existing Perth hospital emergency wait time data, and taps into mobile device geolocation, local maps, and traffic data to give people needing to go to the hospital in a non life-threatening emergency an aggregated travel and wait time.
The team behind the app, Sydney-based Readify, said the idea came in response to the concept of using open and cross-departmental data to benefit its citizens.
Readify said using government open data in smart ways was an initiative the government chief information officer (GCIO) Giles Nunis committed to previously, in a bid to demonstrate that innovation can greatly benefit the public without costing a fortune….(More)”
Health care data as a public utility: how do we get there?
Mohit Kaushal and Margaret Darling at Brookings: “Forty-six million Americans use mobile fitness and health apps. Over half of providers serving Medicare or Medicaid patients are using electronic health records (EHRs). Despite such advances and proliferation of health data and its collection, we are not yet on an inevitable path to unleashing the often-promised “power of data” because data remain proprietary and fragmented among insurers, providers, health record companies, government agencies, and researchers.
Despite the technological integration seen in banking and other industries, health care data has remained scattered and inaccessible. EHRs remain fragmented among 861 distinct ambulatory vendors and 277 inpatient vendors as of 2013. Similarly, insurance claims are stored in the databases of insurers, and information about public health—including information about the social determinants of health, such as housing, food security, safety, and education—is often kept in databases belonging to various governmental agencies. These silos wouldn’t necessarily be a problem, except for the lack of interoperability that has long plagued the health care industry.
For this reason, many are reconsidering if health care data is a public good, provided to all members of the public without profit. This idea is not new. In fact, the Institute of Medicine established the Roundtable on Value and Science-Driven Healthcare, citing that:
“A significant challenge to progress resides in the barriers and restrictions that derive from the treatment of medical care data as a proprietary commodity by the organizations involved. Even clinical research and medical care data developed with public funds are often not available for broader analysis and insights. Broader access and use of healthcare data for new insights require not only fostering data system reliability and interoperability but also addressing the matter of individual data ownership and the extent to which data central to progress in health and health care should constitute a public good.”
Indeed, publicly available health care data holds the potential to unlock many innovations, much like what public goods have done in other industries. As publicly available weather data has shown, the public utility of open access information is not only good for consumers, itis good for businesses…(More)”
Could a tweet or a text increase college enrollment or student achievement?
the Conversation: “Can a few text messages, a timely email or a letter increase college enrollment and student achievement? Such “nudges,” designed carefully using behavioral economics, can be effective.
But when do they work – and when not?
Barriers to success
Consider students who have just graduated high school intending to enroll in college. Even among those who have been accepted to college, 15 percent of low-income students do not enroll by the next fall. For the large share who intend to enroll in community colleges, this number can be as high as 40 percent….
Can a few text messages or a timely email overcome these barriers? My research uses behavioral economics to design low-cost, scalable interventions aimed at improving education outcomes. Behavioral economics suggests several important features to make a nudge effective: simplify complex information, make tasks easier to complete and ensure that support is timely.
So, what makes for an effective nudge?
Improving college enrollment
In 2012, researchers Ben Castleman and Lindsay Page sent 10 text messages to nearly 2,000 college-intending students the summer after high school graduation. These messages provided just-in-time reminders on key financial aid, housing and enrollment deadlines from early July to mid August.
Instead of set meetings with counselors, students could reply to messages and receive on-demand support from college guidance counselors to complete key tasks.
In another intervention – the Expanding College Opportunities Project (ECO) – researchers Caroline Hoxby and Sarah Turner worked to help high-achieving, low-income students enroll in colleges on par with their achievement. The intervention arrived to students as a packet in the mail.
The mailer simplified information by providing a list of colleges tailored to each student’s location along with information about net costs, graduation rates, and application deadlines. Moreover, the mailer included easy-to-claim application fee waivers. All these features reduced both the complexity and cost in applying to a wider range of colleges.
In both cases, researchers found that it significantly improved college outcomes. College enrollment went up by 15 percent in the intervention designed to reduce summer melt for community college students. The ECO project increased the likelihood of admission to a selective college by 78 percent.
…
When there is no impact
While these interventions are promising, there are important caveats.
For instance, our preliminary findings from ongoing research show that information alone may not be enough. We sent emails and letters to more than one hundred thousand college applicants about financial aid and education-related tax benefits. However, we didn’t provide any additional support to help families through the process of claiming these benefits.
In other words, we didn’t provide any support to complete the tasks – no fee waivers, no connection to guidance counselors – just the email and the letter. Without this support to answer questions or help families complete forms to claim the benefits, we found no impact, even when students opened the emails.
More generally, “nudges” often lead to modest impacts and should be considered only a part of the solution. But there’s a dearth of low-cost, scalable interventions in education, and behavioral economics can help.
Identifying the crucial decision points – when applications are due, forms need to be filled out or school choices are made – and supplying the just-in-time support to families is key….(More).”
Moneyballing Criminal Justice
Anne Milgram in the Atlantic: “…One area in which the potential of data analysis is still not adequately realized,however, is criminal justice. This is somewhat surprising given the success of CompStat, a law enforcement management tool that uses data to figure out how police resources can be used to reduce crime and hold law enforcement officials accountable for results. CompStat is widely credited with contributing to New York City’s dramatic reduction in serious crime over the past two decades. Yet data-driven decision-making has not expanded to the whole of the criminal justice system.
But it could. And, in this respect, the front end of the system — the part of the process that runs from arrest through sentencing — is particularly important. Atthis stage, police, prosecutors, defenders, and courts make key choices about how to deal with offenders — choices that, taken together, have an enormous impact on crime. Yet most jurisdictions do not collect or analyze the data necessary to know whether these decisions are being made in a way that accomplishes the most important goals of the criminal justice system: increased public safety,decreased recidivism, reduced cost, and the fair, efficient administration of justice.
Even in jurisdictions where good data exists, a lack of technology is often an obstacle to using it effectively. Police, jails, courts, district attorneys, and public defenders each keep separate information systems, the data from which is almost never pulled together and analyzed in a way that could answer the questions that matter most: Who is in our criminal justice system? What crimes have been charged? What risks do individual offenders pose? And which option would best protect the public and make the best use of our limited resources?
While debates about prison over-crowding, three strikes laws, and mandatory minimum sentences have captured public attention, the importance of what happens between arrest and sentencing has gone largely unnoticed. Even though I ran the criminal justice system in New Jersey, one of the largest states in the country, I had not realized the magnitude of the pretrial issues until I was tasked by theLaura and John Arnold Foundation with figuring out which aspects of criminal justice had the most need and presented the greatest opportunity for reform….
Technology could help us leverage data to identify offenders who will pose unacceptable risks to society if they are not behind bars and distinguish them from those defendants who will have lower recidivism rates if they are supervised in the community or given alternatives to incarceration before trial. Likewise, it could help us figure out which terms of imprisonment, alternatives to incarceration, and other interventions work best–and for whom. And the list does not end there.
The truth is our criminal justice system already makes these decisions every day.But it makes them without knowing whether they’re the right ones. That needs to change. If data is powerful enough to transform baseball, health care, and education, it can do the same for criminal justice….(More)”
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The Small World Initiative: An Innovative Crowdsourcing Platform for Antibiotics
Ana Maria Barral et al in FASEB Journal: “The Small World Initiative™ (SWI) is an innovative program that encourages students to pursue careers in science and sets forth a unique platform to crowdsource new antibiotics. It centers around an introductory biology course through which students perform original hands-on field and laboratory research in the hunt for new antibiotics. Through a series of student-driven experiments, students collect soil samples, isolate diverse bacteria, test their bacteria against clinically-relevant microorganisms, and characterize those showing inhibitory activity. This is particularly relevant since over two thirds of antibiotics originate from soil bacteria or fungi. SWI’s approach also provides a platform to crowdsource antibiotic discovery by tapping into the intellectual power of many people concurrently addressing a global challenge and advances promising candidates into the drug development pipeline. This unique class approach harnesses the power of active learning to achieve both educational and scientific goals…..We will discuss our preliminary student evaluation results, which show the compelling impact of the program in comparison to traditional introductory courses. Ultimately, the mission of the program is to provide an evidence-based approach to teaching introductory biology concepts in the context of a real-world problem. This approach has been shown to be particularly impactful on underrepresented STEM talent pools, including women and minorities….(More)”
Scientists Are Just as Confused About the Ethics of Big-Data Research as You
Sarah Zhang at Wired: “When a rogue researcher last week released 70,000 OkCupid profiles, complete with usernames and sexual preferences, people were pissed. When Facebook researchers manipulated stories appearing in Newsfeeds for a mood contagion study in 2014, people were really pissed. OkCupid filed a copyright claim to take down the dataset; the journal that published Facebook’s study issued an “expression of concern.” Outrage has a way of shaping ethical boundaries. We learn from mistakes.
Shockingly, though, the researchers behind both of those big data blowups never anticipated public outrage. (The OkCupid research does not seem to have gone through any kind of ethical review process, and a Cornell ethics review board approved the Facebook experiment.) And that shows just how untested the ethics of this new field of research is. Unlike medical research, which has been shaped by decades of clinical trials, the risks—and rewards—of analyzing big, semi-public databases are just beginning to become clear.
And the patchwork of review boards responsible for overseeing those risks are only slowly inching into the 21st century. Under the Common Rule in the US, federally funded research has to go through ethical review. Rather than one unified system though, every single university has its own institutional review board, or IRB. Most IRB members are researchers at the university, most often in the biomedical sciences. Few are professional ethicists.
Even fewer have computer science or security expertise, which may be necessary to protect participants in this new kind of research. “The IRB may make very different decisions based on who is on the board, what university it is, and what they’re feeling that day,” says Kelsey Finch, policy counsel at the Future of Privacy Forum. There are hundreds of these IRBs in the US—and they’re grappling with research ethics in the digital age largely on their own….
Or maybe other institutions, like the open science repositories asking researchers to share data, should be picking up the slack on ethical issues. “Someone needs to provide oversight, but the optimal body is unlikely to be an IRB, which usually lacks subject matter expertise in de-identification and re-identification techniques,” Michelle Meyer, a bioethicist at Mount Sinai, writes in an email.
Even among Internet researchers familiar with the power of big data, attitudes vary. When Katie Shilton, an information technology research at the University of Maryland, interviewed 20 online data researchers, she found “significant disagreement” over issues like the ethics of ignoring Terms of Service and obtaining informed consent. Surprisingly, the researchers also said that ethical review boards had never challenged the ethics of their work—but peer reviewers and colleagues had. Various groups like theAssociation of Internet Researchers and the Center for Applied Internet Data Analysis have issued guidelines, but the people who actually have power—those on institutional review boards–are only just catching up.
Outside of academia, companies like Microsoft have started to institute their own ethical review processes. In December, Finch at the Future of Privacy Forum organized a workshop called Beyond IRBs to consider processes for ethical review outside of federally funded research. After all, modern tech companies like Facebook, OkCupid, Snapchat, Netflix sit atop a trove of data 20th century social scientists could have only dreamed up.
Of course, companies experiment on us all the time, whether it’s websites A/B testing headlines or grocery stores changing the configuration of their checkout line. But as these companies hire more data scientists out of PhD programs, academics are seeing an opportunity to bridge the divide and use that data to contribute to public knowledge. Maybe updated ethical guidelines can be forged out of those collaborations. Or it just might be a mess for a while….(More)”