NBER Working Paper by Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainatha: “We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals….(More)”
Why Big Data Is a Big Deal for Cities
John M. Kamensky in Governing: “We hear a lot about “big data” and its potential value to government. But is it really fulfilling the high expectations that advocates have assigned to it? Is it really producing better public-sector decisions? It may be years before we have definitive answers to those questions, but new research suggests that it’s worth paying a lot of attention to.
University of Kansas Prof. Alfred Ho recently surveyed 65 mid-size and large cities to learn what is going on, on the front line, with the use of big data in making decisions. He found that big data has made it possible to “change the time span of a decision-making cycle by allowing real-time analysis of data to instantly inform decision-making.” This decision-making occurs in areas as diverse as program management, strategic planning, budgeting, performance reporting and citizen engagement.
Cities are natural repositories of big data that can be integrated and analyzed for policy- and program-management purposes. These repositories include data from public safety, education, health and social services, environment and energy, culture and recreation, and community and business development. They include both structured data, such as financial and tax transactions, and unstructured data, such as recorded sounds from gunshots and videos of pedestrian movement patterns. And they include data supplied by the public, such as the Boston residents who use a phone app to measure road quality and report problems.
These data repositories, Ho writes, are “fundamental building blocks,” but the challenge is to shift the ownership of data from separate departments to an integrated platform where the data can be shared.
There’s plenty of evidence that cities are moving in that direction and that they already are systematically using big data to make operational decisions. Among the 65 cities that Ho examined, he found that 49 have “some form of data analytics initiatives or projects” and that 30 have established “a multi-departmental team structure to do strategic planning for these data initiatives.”….The effective use of big data can lead to dialogs that cut across school-district, city, county, business and nonprofit-sector boundaries. But more importantly, it provides city leaders with the capacity to respond to citizens’ concerns more quickly and effectively….(More)”
Why We Make Free, Public Information More Accessible
Gabi Fitz and Lisa Brooks in Philantopic: “One of the key roles the nonprofit sector plays in civil society is providing evidence about social problems and their solutions. Given recent changes to policies regarding the sharing of knowledge and evidence by federal agencies, that function is more critical than ever.
Nonprofits deliver more than direct services such as running food banks or providing shelter to people who are homeless. They also collect and share data, evidence, and lessons learned so as to help all of us understand complex and difficult problems.
Those efforts not only serve to illuminate and benchmark our most pressing social problems, they also inform the actions we take, whether at the individual, organizational, community, or policy level. Often, they provide the evidence in “evidence-based” decision making, not to mention the knowledge that social sector organizations and policy makers rely on when shaping their programs and services and individual citizens turn to inform their own engagement.
In January 2017, several U.S. government agencies, including the Environmental Protection Agency and the Departments of Health and Human Services and Agriculture, were ordered by officials of the incoming Trump administration not to share anything that could be construed as controversial through official communication channels such as websites and social media channels. (See “Federal Agencies Told to Halt External Communications.”) Against that backdrop, the nonprofit sector’s interest in generating and sharing evidence has become more urgent than ever…..
Providing access to evidence and lessons learned is always important, but in light of recent events, we believe it’s more necessary than ever. That’s why we are asking for your help in providing — and preserving — access to this critical knowledge base.
Over the next few months, we will be updating and maintaining special collections of non-academic research on the following topics and need lead curators with issue expertise to lend us a hand. IssueLab special collections are an effort to contextualize important segments of the growing evidence base we curate, and are one of the ways we help visitors to the platform learn about nonprofit organizations and resources that may be useful to their work and knowledge-gathering efforts.
Possible special collection topics to be updated or curated:
→ Access to reproductive services (new)
→ Next steps for ACA
→ Race and policing
→ Immigrant detention and deportation
→ Climate change and extractive mining (new)
→ Veterans affairs
→ Gun violence
If you are a researcher, knowledge broker, or service provider in any of these fields of practice, please consider volunteering as a lead curator. …(More)”
Why you should donate your data (as well as your organs) when you die
David Martin Shaw, J. Valérie Grossand Thomas C. Erren in The Conversation: “Most people are aware they can donate their organs when they die. Doing so is very important: Each deceased donor can save several lives if he donates his organs and tissue and they are used for transplantation. Support for organ donation among members of the public is very high – at over 80 percent in some countries, even if many people have not yet gotten around to registering as an organ donor.
But organs aren’t the only thing that you can donate once you’re dead. What about donating your medical data?
Data might not seem important in the way that organs are. People need organs just to stay alive, or to avoid being on dialysis for several hours a day. But medical data are also very valuable – even if they are not going to save someone’s life immediately. Why? Because medical research cannot take place without medical data, and the sad fact is that most people’s medical data are inaccessible for research once they are dead.
For example, working in shifts can be disruptive to one’s circadian rhythms. This is now thought by some to probably cause cancer. A large cohort study involving tens or hundreds of thousands of individuals could help us to investigate different aspects of shift work, including chronobiology, sleep impairment, cancer biology and premature aging. The results of such research could be very important for cancer prevention. However, any such study could currently be hamstrung by the inability to access and analyze participants’ data after they die.
Data rights
While alive, people have certain rights that allow them to control what happens to data concerning them. For example, you can control whether your phone number and address are publicly available, request copies of data held on you by any public bodies and control what Facebook displays about you. When you are dead you will no longer be able to do any of these things, and control of your digital identity after death is a controversial topic. For example, families often cannot access deceased relative’s iTunes purchases, or access the dead person’s Facebook page to indicate that he or she is now deceased.
When it comes to medical records, things become even more complicated. While alive, many people give their consent to participate in medical research, whether it’s a clinical trial of a new drug or a longitudinal study based on medical records. Without their informed consent, such research cannot normally take place. Medical confidentiality is rightly regarded as extremely important, and it can be suspended only with patient consent.
In most jurisdictions, the same applies once persons are dead – with the added problem that consent cannot be obtained from them at that point.
But it would be a serious mistake to assume that everyone wants such strict data confidentiality to persist after death. Just as in life, some people would provide their data for medical research in order to develop new treatments that could help save people’s lives…(More)”
First They Got Sick, Then They Moved Into a Virtual Utopia
Kristen French at BackChannel: “…Today, Second Life is mostly forgotten by the broader public. An estimated 800,000 users are active on a monthly basis, according to Second Life parent company Linden Lab. That’s tiny compared to the 1.86 billion users who are active on Facebook each month.
Yet some communities have quietly continued to thrive in the virtual world. One of these is the disability community, a sundry group whose members include people who are blind or deaf, people with emotional handicaps such as autism and PTSD, and people with conditions that limit their mobility, such as Parkinson’s, cerebral palsy, and multiple sclerosis. There are no official tallies of their numbers, but Wagner James Au, who has writtenextensively about Second Life, estimates they may account for roughly 20 percent of users. Some active members estimate the number higher — at as much as 50 percent.
Unlike traditional gaming, Second Life is governed by few rules. Residents can customize their avatars in an infinite number of ways. They can fly and teleport as easily as they can walk, run, and jump. They can build bespoke homes and islands almost from scratch, and buy and sell wares in virtual stores — from biker gear to bird song to the ability to swim like a mermaid. They can marry a Second Life lover, take a rocket to the moon, or simply tuck themselves into bed at night.
For many disabled residents, who may spend 12 hours a day or more in Second Life, the most important moments and relationships of their lives happen inside the virtual world. For them, the fevered fantasies of a decade ago have become reality: Second Life is where they live.
Second Life’s largest community of disabled residents is clustered on Virtual Ability Island, which is actually an archipelago of five islands — two public and three “residential,” where people can rent or buy homes. It’s the creation of a woman named Alice Krueger. In 2007, Krueger joined Second Life with a few disabled friends she knew from online chat groups.
At the time, she was becoming more isolated as her multiple sclerosis progressed. She’d lost her job, had to drop her volunteer work, and couldn’t even attend her children’s school events. Her friends had stopped coming to see her. She was 58….
As Fran and Barbara tell it, the more time Fran spent in Second Life, the younger she felt in real life. Watching her avatar hike trails and dance gave her the confidence to try things in the physical world that she hadn’t tried in a half decade — like stepping off a curb or standing up without any help. These were small victories, but they felt significant to Fran.
Fran’s story began to spread after Draxtor, a Second Life video artist, filmed a Youtube video about her. (His “World Makers” video series profiles the people behind the avatars in Second Life.) In the film, Fran recounts her experience of Second Life as a quasi-fountain of youth. It also describes the fundraising Fran and Barbara have done for Parkinson’s research through Second Life and Fran’s weekly virtual Parkinson’s support group. Suddenly Fran had a following. Some in Second Life’s disability community now use the term “Fran effect” to describe improvements in real-life functioning that they attribute to their experience in Second Life.
This is not just magical thinking. Abundant research shows imagining movement, without actually moving the body, can have positive effects on motor skills, balance, and learning. The same effects are found in athletes and people who are healthy. Researchers have even found that people who have been paralyzed by severed spinal chord can stimulate regrowth and repair by envisioning their limbs moving over and over again — though it requires great effort and takes time. Studies suggest the therapeutic benefits of virtual reality extend beyond movement disorders — to chronic pain, cognitive functioning in people with ADHD and PTSD, and social skills for people on the autism spectrum….(More)”
Organizational crowdsourcing
This recognition, that in order to uncover new business ideas and innovations, organizations must foster listening cultures and a meritocracy of best thinking, is fueling interest in organizational crowdsourcing — a discipline focused on employee connection, collaboration and ideation. Leaders at companies such as Roche, Bank of the West, Merck, Facebook and IBM, along with countless Silicon Valley companies for whom the “hackathon” is a major cultural event, have embraced employee crowdsourcing as a way to unlock organizational knowledge and promote empathy through technology.
The benefits of internal crowdsourcing are clear. First, it ensures that a company’s understanding of key change drivers and potential strategic priorities is grounded in the organization’s everyday reality and not abstract hypotheses developed by a team of strategists. Second, employees inherently believe in and want to own the implementation of ideas that they generate through crowdsourcing. These are ideas borne of the culture for the culture, and are less likely to run aground on the rocks of employee indifference….
How can this be achieved through organizational crowdsourcing?
There is no out-of-the-box solution. Each campaign has to organically surface areas of focus for further inquiries, develop a framework and set of questions to guide participation and ignite conversations, and then analyze and communicate results in a way that helps bring solutions to life. But there are some key principles that will maximize the success of any crowdsourcing effort.
Obtaining insightful and actionable answers boils down to asking the questions at just the right altitude. If they’re too high up, too broad and open-ended, the usefulness of the feedback will suffer. If the questions are too broad — “How can we make our workplace better?” — you will likely hear responses like “juice bars” and “massage therapists.” If the questions are too narrow — “What kind of lighting do we need in our conference rooms?” — you limit the opportunity of people to use their creativity. However, the answers are likely to spark a conversation if people are asked, “How can we create spaces that allow us to generate ideas more effectively?” Conversation will flow to discussion of breaking down physical barriers in office design, building social “hubs” and investing in live events that allow employees from disparate geographies to meet in person and solve problems together.
On the technology side, crowdsourcing platforms such as Jive Software and UserVoice, among others, make it easy to bring large numbers of employees together to gather, build upon and prioritize new ideas and innovation efforts, from process simplification and product development to the transformation of customer experiences. Respondents can vote on other people’s suggestions and add comments.
By facilitating targeted conversations across times zones, geographies and corporate functions, crowdsourcing makes possible a new way of listening: of harnessing an organization’s collective wisdom to achieve action by a united and inspired employee population. It’s amazing to see the thoughtfulness, precision and energy unleashed by crowdsourcing efforts. People genuinely want to contribute to their company’s success if you open the doors and let them.
Taking a page from the Silicon Valley hackathon, organizational crowdsourcing campaigns are structured as events of limited duration focused on a specific challenge or business problem….(More)”
DataRefuge
“DataRefuge is a public, collaborative project designed to address the following concerns about federal climate and environmental data:
- What are the best ways to safeguard data?
- How do federal agencies play crucial roles in data collection, management, and distribution?
- How do government priorities impact data’s accessibility?
- Which projects and research fields depend on federal data?
- Which data sets are of value to research and local communities, and why?
DataRefuge is also an initiative committed to identifying, assessing, prioritizing, securing, and distributing reliable copies of federal climate and environmental data so that it remains available to researchers. Data collected as part of the #DataRefuge initiative will be stored in multiple, trusted locations to help ensure continued accessibility.
DataRefuge acknowledges–and in fact draws attention to–the fact that there are no guarantees of perfectly safe information. But there are ways that we can create safe and trustworthy copies. DataRefuge is thus also a project to develop the best methods, practices, and protocols to do so.
DataRefuge depends on local communities. We welcome new collaborators who want to organize DataRescue Events or build DataRefuge in other ways.
There are many ways to be involved with building DataRefuge. They’re not mutually exclusive!…(More)”
Mapping open data governance models: Who makes decisions about government data and how?
Ana Brandusescu, Danny Lämmerhirt and Stefaan Verhulst call for a systematic and comparative investigation of the different governance models for open data policy and publication….
“An important value proposition behind open data involves increased transparency and accountability of governance. Yet little is known about how open data itself is governed. Who decides and how? How accountable are data holders to both the demand side and policy makers? How do data producers and actors assure the quality of government data? Who, if any, are data stewards within government tasked to make its data open?
Getting a better understanding of open data governance is not only important from an accountability point of view. If there is a better insight of the diversity of decision-making models and structures across countries, the implementation of common open data principles, such as those advocated by the International Open Data Charter, can be accelerated across countries.
In what follows, we seek to develop the initial contours of a research agenda on open data governance models. We start from the premise that different countries have different models to govern and administer their activities – in short, different ‘governance models’. Some countries are more devolved in their decision making, while others seek to organize “public administration” activities more centrally. These governance models clearly impact how open data is governed – providing a broad patchwork of different open data governance across the world and making it difficult to identify who the open data decision makers and data gatekeepers or stewards are within a given country.
For example, if one wants to accelerate the opening up of education data across borders, in some countries this may fall under the authority of sub-national government (such as states, provinces, territories or even cities), while in other countries education is governed by central government or implemented through public-private partnership arrangements. Similarly, transportation or water data may be privatised, while in other cases it may be the responsibility of municipal or regional government. Responsibilities are therefore often distributed across administrative levels and agencies affecting how (open) government data is produced, and published….(More)”
Corporate Social Responsibility for a Data Age
Stefaan G. Verhulst in the Stanford Social Innovation Review: “Proprietary data can help improve and save lives, but fully harnessing its potential will require a cultural transformation in the way companies, governments, and other organizations treat and act on data….
We live, as it is now common to point out, in an era of big data. The proliferation of apps, social media, and e-commerce platforms, as well as sensor-rich consumer devices like mobile phones, wearable devices, commercial cameras, and even cars generate zettabytes of data about the environment and about us.
Yet much of the most valuable data resides with the private sector—for example, in the form of click histories, online purchases, sensor data, and call data records. This limits its potential to benefit the public and to turn data into a social asset. Consider how data held by business could help improve policy interventions (such as better urban planning) or resiliency at a time of climate change, or help design better public services to increase food security.
Data responsibility suggests steps that organizations can take to break down these private barriers and foster so-called data collaboratives, or ways to share their proprietary data for the public good. For the private sector, data responsibility represents a new type of corporate social responsibility for the 21st century.
While Nepal’s Ncell belongs to a relatively small group of corporations that have shared their data, there are a few encouraging signs that the practice is gaining momentum. In Jakarta, for example, Twitter exchanged some of its data with researchers who used it to gather and display real-time information about massive floods. The resulting website, PetaJakarta.org, enabled better flood assessment and management processes. And in Senegal, the Data for Development project has brought together leading cellular operators to share anonymous data to identify patterns that could help improve health, agriculture, urban planning, energy, and national statistics.
Examples like this suggest that proprietary data can help improve and save lives. But to fully harness the potential of data, data holders need to fulfill at least three conditions. I call these the “the three pillars of data responsibility.”…
The difficulty of translating insights into results points to some of the larger social, political, and institutional shifts required to achieve the vision of data responsibility in the 21st century. The move from data shielding to data sharing will require that we make a cultural transformation in the way companies, governments, and other organizations treat and act on data. We must incorporate new levels of pro-activeness, and make often-unfamiliar commitments to transparency and accountability.
By way of conclusion, here are four immediate steps—essential but not exhaustive—we can take to move forward:
- Data holders should issue a public commitment to data responsibility so that it becomes the default—an expected, standard behavior within organizations.
- Organizations should hire data stewards to determine what and when to share, and how to protect and act on data.
- We must develop a data responsibility decision tree to assess the value and risk of corporate data along the data lifecycle.
- Above all, we need a data responsibility movement; it is time to demand data responsibility to ensure data improves and safeguards people’s lives…(More)”
Understanding Actionable Intelligence for Social Policy
Video on “The Actionable Intelligence (AI) model is a new approach to policy development. The AI approach is supported by Integrated Data Systems (IDS) which link administrative records from multiple agencies to give a broader view of social problems and policy solutions. The use of linked administrative data allows policy analysts, program evaluators and social innovators to test new social program ideas at a much lower cost and higher speed. AI uses these IDS to create a newly informed dialogue among executive leaders, stakeholders and researchers regarding what works best, for whom and in the most cost effective way….(More videos from AISP-UPENN)
Understanding Actionable Intelligence for Social Policy from AISP_UPENN on Vimeo.