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)”
Robot Regulators Could Eliminate Human Error
in the San Francisco Chronicle and Regblog: “Long a fixture of science fiction, artificial intelligence is now part of our daily lives, even if we do not realize it. Through the use of sophisticated machine learning algorithms, for example, computers now work to filter out spam messages automatically from our email. Algorithms also identify us by our photos on Facebook, match us with new friends on online dating sites, and suggest movies to watch on Netflix.
These uses of artificial intelligence hardly seem very troublesome. But should we worry if government agencies start to use machine learning?
Complaints abound even today about the uncaring “bureaucratic machinery” of government. Yet seeing how machine learning is starting to replace jobs in the private sector, we can easily fathom a literal machinery of government in which decisions made by human public servants increasingly become made by machines.
Technologists warn of an impending “singularity,” when artificial intelligence surpasses human intelligence. Entrepreneur Elon Musk cautions that artificial intelligence poses one of our “biggest existential threats.” Renowned physicist Stephen Hawking eerily forecasts that artificial intelligence might even “spell the end of the human race.”
Are we ready for a world of regulation by robot? Such a world is closer than we think—and it could actually be worth welcoming.
Already government agencies rely on machine learning for a variety of routine functions. The Postal Service uses learning algorithms to sort mail, and cities such as Los Angeles use them to time their traffic lights. But while uses like these seem relatively benign, consider that machine learning could also be used to make more consequential decisions. Disability claims might one day be processed automatically with the aid of artificial intelligence. Licenses could be awarded to airplane pilots based on what kinds of safety risks complex algorithms predict each applicant poses.
Learning algorithms are already being explored by the Environmental Protection Agency to help make regulatory decisions about what toxic chemicals to control. Faced with tens of thousands of new chemicals that could potentially be harmful to human health, federal regulators have supported the development of a program to prioritize which of the many chemicals in production should undergo the more in-depth testing. By some estimates, machine learning could save the EPA up to $980,000 per toxic chemical positively identified.
It’s not hard then to imagine a day in which even more regulatory decisions are automated. Researchers have shown that machine learning can lead to better outcomes when determining whether parolees ought to be released or domestic violence orders should be imposed. Could the imposition of regulatory fines one day be determined by a computer instead of a human inspector or judge? Quite possibly so, and this would be a good thing if machine learning could improve accuracy, eliminate bias and prejudice, and reduce human error, all while saving money.
But can we trust a government that bungled the initial rollout of Healthcare.gov to deploy artificial intelligence responsibly? In some circumstances we should….(More)”
Big Data for public policy: the quadruple helix
Julia Lane in the Journal of Policy Analysis and Management: “Data from the federal statistical system, particularly the Census Bureau, have long been a key resource for public policy. Although most of those data have been collected through purposive surveys, there have been enormous strides in the use of administrative records on business (Jarmin & Miranda, 2002), jobs (Abowd, Halti- wanger, & Lane, 2004), and individuals (Wagner & Layne, 2014). Those strides are now becoming institutionalized. The President has allocated $10 million to an Administrative Records Clearing House in his FY2016 budget. Congress is considering a bill to use administrative records, entitled the Evidence-Based Policymaking Commission Act, sponsored by Patty Murray and Paul Ryan. In addition, the Census Bureau has established a Center for “Big Data.” In my view, these steps represent important strides for public policy, but they are only part of the story. Public policy researchers must look beyond the federal statistical system and make use of the vast resources now available for research and evaluation.
All politics is local; “Big Data” now mean that policy analysis can increasingly be local. Modern empirical policy should be grounded in data provided by a network of city/university data centers. Public policy schools should partner with scholars in the emerging field of data science to train the next generation of policy researchers in the thoughtful use of the new types of data; the apparent secular decline in the applications to public policy schools is coincident with the emergence of data science as a field of study in its own right. The role of national statistical agencies should be fundamentally rethought—and reformulated to one of four necessary strands in the data infrastructure; that of providing benchmarks, confidentiality protections, and national statistics….(More)”
Society’s biggest problems need more than a nudge
So-called “nudge units” are popping up in governments all around the world.
The best-known examples include the U.K.’s Behavioural Insights Team, created in 2010, and the White House-based Social and Behavioral Sciences Team, introduced by the Obama administration in 2014. Their mission is to leverage findings from behavioral science so that people’s decisions can be nudged in the direction of their best intentions without curtailing their ability to make choices that don’t align with their priorities.
Overall, these – and other – governments have made important strides when it comes to using behavioral science to nudge their constituents into better choices.
Yet, the same governments have done little to improve their own decision-making processes. Consider big missteps like the Flint water crisis. How could officials in Michigan decide to place an essential service – safe water – and almost 100,000 people at risk in order to save US$100 per day for three months? No defensible decision-making process should have allowed this call to be made.
When it comes to many of the big decisions faced by governments – and the private sector – behavioral science has more to offer than simple nudges.
Behavioral scientists who study decision-making processes could also help policy-makers understand why things went wrong in Flint, and how to get their arms around a wide array of society’s biggest problems – from energy transitions to how to best approach the refugee crisis in Syria.
When nudges are enough
The idea of nudging people in the direction of decisions that are in their own best interest has been around for a while. But it was popularized in 2008 with the publication of the bestseller “Nudge“ by Richard Thaler of the University of Chicago and Cass Sunstein of Harvard.
A common nudge goes something like this: if we want to eat better but are having a hard time doing it, choice architects can reengineer the environment in which we make our food choices so that healthier options are intuitively easier to select, without making it unrealistically difficult to eat junk food if that’s what we’d rather do. So, for example, we can shelve healthy foods at eye level in supermarkets, with less-healthy options relegated to the shelves nearer to the floor….
Sometimes a nudge isn’t enough
Nudges work for a wide array of choices, from ones we face every day to those that we face infrequently. Likewise, nudges are particularly well-suited to decisions that are complex with lots of different alternatives to choose from. And, they are advocated in situations where the outcomes of our decisions are delayed far enough into the future that they feel uncertain or abstract. This describes many of the big decisions policy-makers face, so it makes sense to think the solution must be more nudge units.
But herein lies the rub. For every context where a nudge seems like a realistic option, there’s at least another context where the application of passive decision support would be either be impossible – or, worse, a mistake.
Take, for example, the question of energy transitions. These transitions are often characterized by the move from infrastructure based on fossil fuels to renewables to address all manner of risks, including those from climate change. These are decisions that society makes infrequently. They are complex. And, the outcomes – which are based on our ability to meet conflicting economic, social and environmental objectives – will be delayed.
But, absent regulation that would place severe restrictions on the kinds of options we could choose from – and which, incidentally, would violate the freedom-of-choice tenet of choice architecture – there’s no way to put renewable infrastructure options at proverbial eye level for state or federal decision-makers, or their stakeholders.
Simply put, a nudge for a decision like this would be impossible. In these cases, decisions have to be made the old-fashioned way: with a heavy lift instead of a nudge.
Complex policy decisions like this require what we call active decision support….(More)”
Where are Human Subjects in Big Data Research? The Emerging Ethics Divide
Paper by Jacob Metcalf and Kate Crawford: “There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. These discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule — the primary regulation governing human-subjects research in the U.S. — is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research, and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, but they problematically exclude many data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for big data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about human-subjects research ethics in data science,critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce….(More)”
Citizen Generated Data In Practice
DataShift: “No-one can communicate the importance of citizen-generated data better than those who are actually working with it. At DataShift, we want to highlight the civil society organisations who have told us about the tangible results they have achieved through innovative approaches to harnessing data from citizens.
Each essay profiles the objectives, challenges and targets of an organisation using data generated by citizens to achieve their goals. We hope that the essays in this collection can help more people feel more confident about asking questions of the data that affects their lives, and taking a hands-on approach to creating it. (More)”
Is behavioural economics ready to save the world?
Book review by Trenton G Smith of Behavioral Economics and Public Health : “Modern medicine has long doled out helpful advice to ailing patients about not only drug treatments, but also diet, exercise, alcohol abuse, and many other lifestyle decisions. And for just as long, patients have been failing to follow doctors’ orders. Many of today’s most pressing public health problems would disappear if people would just make better choices.
Enter behavioural economics. A fairly recent offshoot of the dismal science, behavioural economics aims to take the coldly rational decision makers who normally populate economic theories, and instil in them a host of human foibles. Neoclassical (ie, conventional) economics, after all is the study of optimising behaviour in the presence of material constraints—why not add constraints on cognitive capacity, or self-control, or susceptibility to the formation of bad habits? The hope is that by incorporating insights from other behavioural sciences (most notably cognitive psychology and neuroscience) while retaining the methodological rigour of neoclassical economics, behavioural economics will yield a more richly descriptive theory of human behaviour, and generate new and important insights to better inform public policy.
Policy makers have taken notice. In an era in which free-market rhetoric dominates the political landscape, the idea that small changes to public health policies might serve to nudge consumers towards healthier behaviours holds great appeal. Even though some (irrational) consumers might be better off, the argument goes, if certain unhealthy food products were banned (or worse, taxed), this approach would infringe on the rights of the many consumers who want to indulge occasionally, and fully understand the consequences. If governments could instead use evidence from consumer science to make food labels more effective, or to improve the way that healthy foods are presented in school cafeterias, more politically unpalatable interventions in the marketplace might not be needed. This idea, dubbed “libertarian paternalism” by Richard Thaler and Cass Sunstein, has been pursued with gusto in both the UK (David Cameron’s Government formed the Behavioural Insights Team—unofficially described as the Nudge Unit) and the USA (where Sunstein spent time in the Obama administration’s Office of Information and Regulatory Affairs).
Whatever public health practitioners might think about these developments—or indeed, of economics as a discipline—this turn of events has rather suddenly given scholars at the cutting edge of consumer science an influential voice in the regulatory process, and some of the best and brightest have stepped up to contribute. Behavioral Economics & Public Health (edited by Christina Roberto and Ichiro Kawachi) is the product of a 2014 Harvard University exploratory workshop on applying social science insights to public health. As might be expected in a volume that aims to bring together two such inherently multidisciplinary fields, the book’s 11 chapters offer an eclectic mix of perspectives. The editors begin with an excellent overview of the field of behavioural economics and its applications to public health, and an economic perspective can also be found in four of the other chapters: Justin White and William Dow write about intertemporal choice, Kristina Lewis and Jason Block review the use of incentives to promote health, Michael Sanders and Michael Hallsworth describe their experience working within the UK’s Behavioural Insights Team, and Frederick Zimmerman concludes with a thoughtful critique of the field of behavioural economics. The other contributions are largely from the perspectives of psychology and marketing: Dennis Runger and Wendy Wood discuss habit formation, Rebecca Ferrer and colleagues emphasise the importance of emotion in decision making, Brent McFerran discusses social norms in the context of obesity, Jason Riis and Rebecca Ratner explain why some public health communication strategies are more effective than others, and Zoe Chance and colleagues and Brian Wansink offer frameworks for designing environments (eg, in schools and workplaces) that are conducive to healthy choices.
This collection of essays holds many hidden gems, but the one that surprised me the most was the attention given (by Runger and Wood briefly, and Zimmerman extensively) to a dirty little secret that behavioural economists rarely mention: once it is acknowledged that sometimes-irrational consumers can be manipulated into making healthy choices, it does not require much of a leap to conclude that business interests can—and do—use the same methods to push back in the other direction. This conclusion leads Zimmerman to a discussion of power in the marketplace and in our collective political economy, and to a call to action on these larger structural issues in society that neoclassical theory has long neglected….(More; Book)
Critics allege big data can be discriminatory, but is it really bias?
Pradip Sigdyal at CNBC: “…The often cited case of big data discrimination points to a research conducted few years ago by Latanya Sweeny, who heads the Data Privacy Lab at Harvard University.
The case involves Google ad results when searching for certain kinds of names on the internet. In her research, Sweeney found that distinct sounding names often associated with blacks showed up with a disproportionately higher number of arrest record ads compared to white sounding names by roughly 18 percent of the time. Google has since fixed the issue, although they never publicly stated what they did to correct the problem.
The proliferation of big data in the last few years has seen other allegations of improper use and bias. These allegations run the gamut, from online price discrimination and consequences of geographic targeting to the controversial use of crime predicting technology by law enforcement, and lack of sufficient representative[data] sampleused in some public works decisions.
The benefits of big data need to be balanced with the risks associated with applying modern technologies to address societal issues. Yet data advocates believe that democratization of data has in essence givenpower to the people to affect change by transferring ‘tribal knowledge’ from experts to data-savvy practitioners.
Big data is here to stay
According to some advocates, the problem is not so much that ‘big data discriminates’, but that failures by data professionals risk misinterpreting the findings at the heart of data mining and statistical learning. They add that the benefits far outweigh the concerns.
“In my academic research and industry consulting, I have seen tremendous benefits accruing to firms, organizations and consumers alike from the use of data-driven decision-making, data science, and business analytics,” Anindya Ghose, the director of Center for Business Analytics at New York University’s Stern School of Business, said.
“To be perfectly honest, I do not at all understand these big-data cynics who engage in fear mongering about the implications of data analytics,” Ghose said.
“Here is my message to the cynics and those who keep cautioning us: ‘Deal with it, big data analytics is here to stay forever’.”…(More)”
OSoMe: The IUNI observatory on social media
Clayton A Davis et al at Peer J. PrePrint: “The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.”…(More)”