Book edited by Vinod Kumar, T. M.: “The present book highlights studies that show how smart cities promote urban economic development. The book surveys the state of the art of Smart City Economic Development through a literature survey. The book uses 13 in depth city research case studies in 10 countries such as the North America, Europe, Africa and Asia to explain how a smart economy changes the urban spatial system and vice versa. This book focuses on exploratory city studies in different countries, which investigate how urban spatial systems adapt to the specific needs of smart urban economy. The theory of smart city economic development is not yet entirely understood and applied in metropolitan regional plans. Smart urban economies are largely the result of the influence of ICT applications on all aspects of urban economy, which in turn changes the land-use system. It points out that the dynamics of smart city GDP creation takes ‘different paths,’ which need further empirical study, hypothesis testing and mathematical modelling. Although there are hypotheses on how smart cities generate wealth and social benefits for nations, there are no significant empirical studies available on how they generate urban economic development through urban spatial adaptation. This book with 13 cities research studies is one attempt to fill in the gap in knowledge base….(More)”
Everyday ‘Placebo Buttons’ Create Semblance of Control
Each of these seemingly disconnected everyday buttons you pressed may have something in common: it is quite possible that none of them did a thing to influence the world around you. Any perceived impact may simply have been imaginary, a placebo effect giving you the illusion of control.
In the early 2000s, New York City transportation officials finally admitted what many had suspected: the majority of crosswalk buttons in the city are completely disconnected from the traffic light system. Thousands of these initially worked to request a signal change but most no longer do anything, even if their signage suggests otherwise.
Naturally, a number of street art projects have popped up around the humorous futility of pedestrians pressing placebo buttons:
Crosswalk buttons were originally introduced to NYC during the 1960s. At the time, there was less congestion and it made sense to leave green lights on for major thoroughfares until cross traffic came along … or until a pedestrian wanting to cross the street pushed a button.
Today, a combination of carefully orchestrated automation and higher traffic has made most of these buttons obsolete. Citywide, there are around 100 crosswalk buttons that still work in NYC but close to 1,000 more that do nothing at all. So why not take them down? Removing the remaining nonfunctional buttons would cost the city millions, a potential waste of already limited funds for civic infrastructure….(More)”
How Big Data Analytics is Changing Legal Ethics
Renee Knake at Bloomberg Law: “Big data analytics are changing how lawyers find clients, conduct legal research and discovery, draft contracts and court papers, manage billing and performance, predict the outcome of a matter, select juries, and more. Ninety percent of corporate legal departments, law firms, and government lawyers note that data analytics are applied in their organizations, albeit in limited ways, according to a 2015 survey. The Legal Services Corporation, the largest funder of civil legal aid for low-income individuals in the United States, recommended in 2012 that all states collect and assess data on case progress/outcomes to improve the delivery of legal services. Lawyers across all sectors of the market increasingly recognize how big data tools can enhance their work.
A growing literature advocates for businesses and governmental bodies to adopt data ethics policies, and many have done so. It is not uncommon to find data-use policies prominently displayed on company or government websites, or required a part of a click-through consent before gaining access to a mobile app or webpage. Data ethics guidelines can help avoid controversies, especially when analytics are used in potentially manipulative or exploitive ways. Consider, for example, Target’s data analytics that uncovered a teen’s pregnancy before her father did, or Orbitz’s data analytics offered pricier hotels to Mac users. These are just two of numerous examples in recent years where companies faced criticism for how they used data analytics.
While some law firms and legal services organizations follow data-use policies or codes of conduct, many do not. Perhaps this is because the legal profession was not transformed as early or rapidly as other industries, or because until now, big data in legal was largely limited to e-discovery, where the data use is confined to the litigation and is subject to judicial oversight. Another reason may be that lawyers believe their rules of professional conduct provide sufficient guidance and protection. Unlike other industries, lawyers are governed by a special code of ethical obligations to clients, the justice system, and the public. In most states, this code is based in part upon the American Bar Association (ABA) Model Rules of Professional Conduct, though rules often vary from jurisdiction to jurisdiction. Several of the Model Rules are relevant to big data use. That said, the Model Rules are insufficient for addressing a number of fundamental ethical concerns.
At the moment, legal ethics for big data analytics is at best an incomplete mix of professional conduct rules and informal policies adopted by some, but not all law practices. Given the increasing prevalence of data analytics in legal services, lawyers and law students should be familiar not only with the relevant professional conduct rules, but also the ethical questions left unanswered. Listed below is a brief summary of both, followed by a proposed legal ethics agenda for data analytics. …
Questions Unanswered by Lawyer Ethics Rules
Access/Ownership. Who owns the original data — the individual source or the holder of the pooled information? Who owns the insights drawn from its analysis? Who should receive access to the data compilation and the results?
Anonymity/Identity. Should all personally identifiable or sensitive information be removed from the data? What protections are necessary to respect individual autonomy? How should individuals be able to control and shape their electronic identity?
Consent. Should individuals affirmatively consent to use of their personal data? Or is it sufficient to provide notice, perhaps with an opt-out provision?
Privacy/Security. Should privacy be protected beyond the professional obligation of client confidentiality? How should data be secured? The ABA called upon private and public sector lawyers to implement cyber-security policies, including data use, in a 2012resolution and produced a cyber-security handbook in 2013.
Process. How involved should lawyers be in the process of data collection and analysis? In the context of e-discovery, for example, a lawyer is expected to understand how documents are collected, produced, and preserved, or to work with a specialist. Should a similar level of knowledge be required for all forms of data analytics use?
Purpose. Why was the data first collected from individuals? What is the purpose for the current use? Is there a significant divergence between the original and secondary purposes? If so, is it necessary for the individuals to consent to the secondary purpose? How will unintended consequences be addressed?
Source. What is the source of the data? Did the lawyer collect it directly from clients, or is the lawyer relying upon a third-party source? Client-based data is, of course, subject to the lawyer’s professional conduct rules. Data from any source should be trustworthy, reasonable, timely, complete, and verifiable….(More)”
Open Data for Social Change and Sustainable Development
Special issue of the Journal of Community Informatics edited by Raed M. Sharif and Francois Van Schalkwyk: “As the second phase of the Emerging Impacts of Open Data in Developing Countries (ODDC) drew to a close, discussions started on a possible venue for publishing some of the papers that emerged from the research conducted by the project partners. In 2012 the Journal of Community Informatics published a special issue titled ‘Community Informatics and Open Government Data’. Given the journal’s previous interest in the field of open data, its established reputation and the fact that it is a peer-reviewed open access journal, the Journal of Community Informatics was approached and agreed to a second special issue with a focus on open data. A closed call for papers was sent out to the project research partners. Shortly afterwards, the first Open Data Research Symposium was held ahead of the International Open Data Conference 2015 in Ottawa, Canada. For the first time, a forum was provided to academics and researchers to present papers specifically on open data. Again there were discussions about an appropriate venue to publish selected papers from the Symposium. The decision was taken by the Symposium Programme Committee to invite the twenty plus presenters to submit full papers for consideration in the special issue.
The seven papers published in this special issue are those that were selected through a double-blind peer review process. Researchers are often given a rough ride by open data advocates – the research community is accused of taking too long, not being relevant enough and of speaking in tongues unintelligible to social movements and policy-makers. And yet nine years after the ground-breaking meeting in Sebastopol at which the eight principles of open government data were penned, seven after President Obama injected political legitimacy into a movement, and five after eleven nation states formed the global Open Government Partnership (OGP), which has grown six-fold in membership; an email crosses our path in which the authors of a high-level report commit to developing a comprehensive understanding of a continental open data ecosystem through an examination of open data supply. Needless to say, a single example is not necessarily representative of global trends in thinking about open data. Yet, the focus on government and on the supply of open data by open data advocates – with little consideration of open data use, the differentiation of users, intermediaries, power structures or the incentives that propel the evolution of ecosystems – is still all too common. Empirical research has already revealed the limitations of ‘supply it and they will use it’ open data practices, and has started to fill critical knowledge gaps to develop a more holistic understanding of the determinants of effective open data policy and practice. As open data policies and practices evolve, the need to capture the dynamics of this evolution and to trace unfolding outcomes becomes critical to advance a more efficient and progressive field of research and practice. The trajectory of the existing body of literature on open data and the role of public authorities, both local and national, in the provision of open data
As open data policies and practices evolve, the need to capture the dynamics of this evolution and to trace unfolding outcomes becomes critical to advance a more efficient and progressive field of research and practice. The trajectory of the existing body of literature on open data and the role of public authorities, both local and national, in the provision of open data is logical and needed in light of the central role of government in producing a wide range of types and volumes of data. At the same time, the complexity of open data ecosystem and the plethora of actors (local, regional and global suppliers, intermediaries and users) makes a compelling case for opening avenues for more diverse discussion and research beyond the supply of open data. The research presented in this special issue of the Journal of Community Informatics touches on many of these issues, sets the pace and contributes to the much-needed knowledge base required to promote the likelihood of open data living up to its promise. … (More)”
How Medical Crowdsourcing Empowers Patients & Doctors
Rob Stretch at Rendia: “Whether you’re a solo practitioner in a rural area, or a patient who’s bounced from doctor to doctor with adifficult–to-diagnose condition, there are many reasons why you might seek out expert medical advice from a larger group. Fortunately, in 2016, seeking feedback from other physicians or getting a second opinion is as easy as going online.
“Medical crowdsourcing” sites and apps are gathering steam, from provider-only forums likeSERMOsolves and Figure 1, to patient-focused sites like CrowdMed. They share the same mission of empowering doctors and patients, reducing misdiagnosis, and improving medicine. Is crowdsourcing the future of medicine? Read on to find out more.
Fixing misdiagnosis
An estimated 10 percent to 20 percent of medical cases are misdiagnosed, even more than drug errors and surgery on the wrong patient or body part, according to the National Center for Policy Analysis. And diagnostic errors are the leading cause of malpractice litigation. Doctors often report that with many of their patient cases, they would benefit from the support and advice of their peers.
The photo-sharing app for health professionals, Figure 1, is filling that need. Since we reported on it last year, the app has reached 1 million users and added a direct-messaging feature. The app is geared towards verified medical professionals, and goes to great lengths to protect patient privacy in keeping with HIPAAlaws. According to co-founder and CEO Gregory Levey, an average of 10,000 unique users check in toFigure 1 every hour, and medical professionals and students in 190 countries currently use the app.
Using Figure 1 to crowdsource advice from the medical community has saved at least one life. EmilyNayar, a physician assistant in rural Oklahoma and a self-proclaimed “Figure 1 addict,” told Wired magazine that because of photos she’d seen on the app, she was able to correctly diagnose a patient with shingles meningitis. Another doctor had misdiagnosed him previously, and the wrong medication could have killed him.
Collective knowledge at zero cost
In addition to serving as “virtual colleagues” for isolated medical providers, crowdsourcing forums can pool knowledge from an unprecedented number of doctors in different specialties and even countries,and can do so very quickly.
When we first reported on SERMO, the company billed itself as a “virtual doctors’ lounge.” Now, the global social network with 600,000 verified, credentialed physician members has pivoted to medical crowdsourcing with SERMOsolves, one of its most popular features, according to CEO Peter Kirk.
“Crowdsourcing patient cases through SERMOsolves is an ideal way for physicians to gain valuable information from the collective knowledge of hundreds of physicians instantly,” he said in a press release.According to SERMO, 3,500 challenging patient cases were posted in 2014, viewed 700,000 times, and received 50,000 comments. Most posted cases received responses within 1.5 hours and were resolved within a day. “We have physicians from more than 96 specialties and subspecialties posting on the platform, working together to share their valuable insights at zero cost to the healthcare system.”
While one early user of SERMO wrote on KevinMD.com that he felt the site’s potential was overshadowed by the anonymous rants and complaining, other users have noted that the medical crowdsourcing site has,like Figure 1, directly benefitted patients.
In an article on PhysiciansPractice.com, Richard Armstrong, M.D., cites the example of a family physician in Canada who posted a case of a young girl with an E. coli infection. “Physicians from around the world immediately began to comment and the recommendations resulted in a positive outcome for the patient.This instance offered cross-border learning experiences for the participating doctors, not only regarding the specific medical issue but also about how things are managed in different health systems,” wrote Dr.Armstrong.
Patients get proactive
While patients have long turned to social media to (questionably) crowdsource their medical queries, there are now more reputable sources than Facebook.
Tech entrepreneur Jared Heyman launched the health startup CrowdMed in 2013 after his sister endured a “terrible, undiagnosed medical condition that could have killed her,” he told the Wall Street Journal. She saw about 20 doctors over three years, racking up six-figure medical bills. The NIH Undiagnosed DiseaseProgram finally gave her a diagnosis: fragile X-associated primary ovarian insufficiency, a rare disease that affects just 1 in 15,000 women. A hormone patch resolved her debilitating symptoms….(More)”
How Technology Can Restore Our Trust in Democracy
Cenk Sidar in Foreign Policy: “The travails of the Arab Spring, the rise of the Islamic State, and the upsurge of right-wing populism throughout the countries of West all demonstrate a rising frustration with the liberal democratic order in the years since the 2008 financial crisis. There is a growing intellectual consensus that the world is sailing into uncharted territory: a realm marked by authoritarianism, shallow populism, and extremism.
One way to overcome this global resentment is to use the best tools we have to build a more inclusive and direct democracy. Could new technologies such as Augmented Reality (AR), Virtual Reality (VR), data analytics, crowdsourcing, and Blockchain help to restore meaningful dialogue and win back people’s hearts and minds?
Underpinning our unsettling current environment is an irony: Thanks to modern communication technology, the world is more connected than ever — but average people feel more disconnected. In the United States, polls show that trust in government is at a 50-year low. Frustrated Trump supporters and the Britons who voted for Brexit both have a sense of having “lost out” as the global elite consolidates its power and becomes less responsive to the rest of society. This is not an irrational belief: Branko Milanovic, a leading inequality scholar, has found that people in the lower and middle parts of rich countries’ income distributions have been the losers of the last 15 years of globalization.
The same 15 years have also brought astounding advances in technology, from the rise of the Internet to the growing ubiquity of smartphones. And Western society has, to some extent, struggled to find its bearings amid this transition. Militant groups seduce young people through social media. The Internet enables consumers to choose only the news that matches their preconceived beliefs, offering a bottomless well of partisan fury and conspiracy theories. Cable news airing 24/7 keeps viewers in a state of agitation. In short, communication technologies that are meant to bring us together end up dividing us instead (and not least because our politicians have chosen to game these tools for their own advantage).
It is time to make technology part of the solution. More urgently than ever, leaders, innovators, and activists need to open up the political marketplace to allow technology to realize its potential for enabling direct citizen participation. This is an ideal way to restore trust in the democratic process.
As the London School of Economics’ Mary Kaldor put it recently: “The task of global governance has to be reconceptualized to make it possible for citizens to influence the decisions that affect their lives — to reclaim substantive democracy.” One notable exception to the technological disconnect has been fundraising, as candidates have tapped into the Internet to enable millions of average voters to donate small sums. With the right vision, however, technological innovation in politics could go well beyond asking people for money….(More)”
Make Algorithms Accountable
Julia Angwin in The New York Times: “Algorithms are ubiquitous in our lives. They map out the best route to our destination and help us find new music based on what we listen to now. But they are also being employed to inform fundamental decisions about our lives.
Companies use them to sort through stacks of résumés from job seekers. Credit agencies use them to determine our credit scores. And the criminal justice system is increasingly using algorithms to predict a defendant’s future criminality.
Those computer-generated criminal “risk scores” were at the center of a recent Wisconsin Supreme Court decision that set the first significant limits on the use of risk algorithms in sentencing.
The court ruled that while judges could use these risk scores, the scores could not be a “determinative” factor in whether a defendant was jailed or placed on probation. And, most important, the court stipulated that a pre sentence report submitted to the judge must include a warning about the limits of the algorithm’s accuracy.
This warning requirement is an important milestone in the debate over how our data-driven society should hold decision-making software accountable.But advocates for big data due process argue that much more must be done to assure the appropriateness and accuracy of algorithm results.
An algorithm is a procedure or set of instructions often used by a computer to solve a problem. Many algorithms are secret. In Wisconsin, for instance,the risk-score formula was developed by a private company and has never been publicly disclosed because it is considered proprietary. This secrecy has made it difficult for lawyers to challenge a result.
The credit score is the lone algorithm in which consumers have a legal right to examine and challenge the underlying data used to generate it. In 1970,President Richard M. Nixon signed the Fair Credit Reporting Act. It gave people the right to see the data in their credit reports and to challenge and delete data that was inaccurate.
For most other algorithms, people are expected to read fine-print privacy policies, in the hopes of determining whether their data might be used against them in a way that they wouldn’t expect.
“We urgently need more due process with the algorithmic systems influencing our lives,” says Kate Crawford, a principal researcher atMicrosoft Research who has called for big data due process requirements.“If you are given a score that jeopardizes your ability to get a job, housing or education, you should have the right to see that data, know how it was generated, and be able to correct errors and contest the decision.”
The European Union has recently adopted a due process requirement for data-driven decisions based “solely on automated processing” that“significantly affect” citizens. The new rules, which are set to go into effect in May 2018, give European Union citizens the right to obtain an explanation of automated decisions and to challenge those decisions. However, since the European regulations apply only to situations that don’t involve human judgment “such as automatic refusal of an online credit application or e-recruiting practices without any human intervention,” they are likely to affect a narrow class of automated decisions. …More recently, the White House has suggested that algorithm makers police themselves. In a recent report, the administration called for automated decision-making tools to be tested for fairness, and for the development of“algorithmic auditing.”
But algorithmic auditing is not yet common. In 2014, Eric H. Holder Jr.,then the attorney general, called for the United States SentencingCommission to study whether risk assessments used in sentencing were reinforcing unjust disparities in the criminal justice system. No study was done….(More)”
Are Crowds Wise? Engagement Over Reliance
Bruce Muirhead at Mindhive: “Crowdsourcing is developing into a mega-trend. It has begun an inexorable shift from the periphery to the mainstream of policy and problem solving methodology. We’ve heard countless times the virtue of crowds and the inherent advantages regarding access to knowledge, transparency, accountability and efficiency – yet all of these advantages rest on the simple assumption that the crowd is wise.
In the fast growing industry of crowdsourcing platforms and in society more generally we can see a growing acceptance by organisations and users alike that the crowds they are engaging with have some common failings. For instance, when addressing a specific problem there is need to consider and discount alternatives before a solution can be arrived at. In a crowd of one it is quite simple to assess the value of each competing solution and evaluate relative to these assessment the most appropriate response. Crowds are obviously not a homogenous grouping capable of relative comparison to the same degree an individual or small group can due to the fact they lack an objective set of priorities or objectives to evaluate them against. A diverse crowd from varied backgrounds will pull the preference of solution in many different directions, In the same way a machine with many moving parts is more likely to fail, a crowd with high levels of expertise, diversity of preference and variance of background is more likely to fail to reach consensus or compromise through logic and reasoning. This presents an interesting catch-22 as many crowdsourcing methodologies recommend involving a large number of varied opinions and backgrounds to enhance the originality and disruptiveness of a solution. However, such levels of disruption also imbalance the internal reasoning of the crowd and make it difficult to develop a nuanced, targeted solution to a challenge. Of course, organisations that seek to engage with crowds can mitigate these risks by developing clear objective standards of reference and outlining and priorities available to the crowd.
Additionally, in a year where the force of a crowd has propelled a man such as Donald Trump to a position that may feasible see him elected President of the United States – how can any argue that crowds are wise? Stephen Walt of Foreign Policy argues that such crowds act as such in a political context due a failing of trusting, in turn resulting from a failure of accountability. ….While crowds don’t always make wise choices, they are neither inherently wise nor unwise groups. There is doubtless intelligence in crowds – what we need to figure out and continue to develop is the process through which we can leverage it to develop more targeted solutions and involving the crowd more effectively….(More)”
From killing machines to agents of hope: the future of drones in Africa
Zoe Flood in The Guardian: “Some are killing machines. Others are pesky passions of the weekend hobbyist. As such, drones have not always been welcomed in our skies.
Across Africa, however, projects are being launched that could revolutionise medical supply chains and commercial deliveries, combat poaching and provide other solutions for an overburdened, underdeveloped continent.
In Rwanda, as in many other African countries, the rainy season makes already difficult roads between smaller towns and villages all but impassable. Battered trucks struggle through the mud, and in some cases even more agile motorbikes and foot traffic are unable get through.
“Rwanda is essentially a rural country. Lots of blood products cannot be stocked at every health centre. At best it can take four to six hours to get supplies through,” says the technology minister, Jean Philbert Nsengimana.
“For mothers giving birth, postpartum haemorrhaging, or bleeding post-delivery, happens quite often. It may not be possible to prevent. Then what is needed is a quick and rapid intervention.”
“This technology has the potential to erase barriers to access for countless critical medicines and save lives on a scale not previously possible,” says Keller Rinaudo, Zipline’s chief executive, which is staffed by experienced aerospace engineers including those who have worked at SpaceX, Boeing and Nasa.
“While there are a number of potential applications for this technology, we’re keenly focused on using it to save lives.”…
Drones are being tested in other emerging economies. Matternet, another Silicon Valley startup, has run pilots moving samples from rural clinics to a laboratory inPapua New Guinea and is launching a small medical delivery network inDominican Republic.
The company is also working with Unicef in Malawi to develop a project using UAVs to carry blood samples from infants born to HIV-positive parents, underscoring the physical and geographical challenges that are present across much of the continent.
Some frontline health workers are supportive….(More)”
Does Crime-Predicting Software Bias Judges? Unfortunately, There’s No Data
Rose Eveleth at Motherboard: “For centuries judges have had to make guesses about the people in front of them.Will this person commit a crime again? Or is this punishment enough to deter them?Do they have the support they need at home to stay safe and healthy and away from crime? Or will they be thrust back into a situation that drives them to their old ways? Ultimately, judges have to guess.
But recently, judges in states including California and Florida have been given a new piece of information to aid in that guess work: a “risk assessment score” determined by an algorithm. These algorithms take a whole suite of variables into account, and spit out a number (usually between 1 and 10) that estimates the risk that the person in question will wind up back in jail.
If you’ve read this column before, you probably know where this is going. Algorithms aren’t unbiased, and a recent ProPublica investigation suggests what researchers have long been worried about: that these algorithms might contain latent racial prejudice. According to ProPublica’s evaluation of a particular scoring method called the COMPAS system, which was created by a company called Northpointe, people of color are more likely to get higher scores than white people for essentially the same crimes.
Bias against folks of color isn’t a new phenomenon in the judicial system. (This might be the understatement of the year.) There’s a huge body of research that shows that judges, like all humans, are biased. Plenty of studies have shown that for the same crime, judges are more likely to sentence a black person more harshly than a white person. It’s important to question biases of all kinds, both human and algorithmic, but it’s also important to question them in relation to one another. And nobody has done that.
I’ve been doing some research of my own into these recidivism algorithms, and whenI read the ProPublica story, I came out with the same question I’ve had since I started looking into this: these algorithms are likely biased against people of color. But so are judges. So how do they compare? How does the bias present in humans stack up against the bias programmed into algorithms?
This shouldn’t be hard to find out: ideally you would divide judges in a single county in half, and give one half access to a scoring system, and have the other half carry on as usual. If you don’t want to A/B test within a county—and there are some questions about whether that’s an ethical thing to do—then simply compare two counties with similar crime rates, in which one county uses rating systems and the other doesn’t. In either case, it’s essential to test whether these algorithmic recidivism scores exacerbate, reduce, or otherwise change existing bias.
Most of the stories I’ve read about these sentencing algorithms don’t mention any such studies. But I assumed that they existed, they just didn’t make the cut in editing.
I was wrong. As far as I can find, and according to everybody I’ve talked to in the field,nobody has done this work, or anything like it. These scores are being used by judges to help them sentence defendants and nobody knows whether the scores exacerbate existing racial bias or not….(More)”