New paper by Beth Simone Noveck, The GovLab, for Democracy: “In the early 2000s, the Air Force struggled with a problem: Pilots and civilians were dying because of unusual soil and dirt conditions in Afghanistan. The soil was getting into the rotors of the Sikorsky UH-60 helicopters and obscuring the view of its pilots—what the military calls a “brownout.” According to the Air Force’s senior design scientist, the manager tasked with solving the problem didn’t know where to turn quickly to get help. As it turns out, the man practically sitting across from him had nine years of experience flying these Black Hawk helicopters in the field, but the manager had no way of knowing that. Civil service titles such as director and assistant director reveal little about skills or experience.
In the fall of 2008, the Air Force sought to fill in these kinds of knowledge gaps. The Air Force Research Laboratory unveiled Aristotle, a searchable internal directory that integrated people’s credentials and experience from existing personnel systems, public databases, and users themselves, thus making it easy to discover quickly who knew and had done what. Near-term budgetary constraints killed Aristotle in 2013, but the project underscored a glaring need in the bureaucracy.
Aristotle was an attempt to solve a challenge faced by every agency and organization: quickly locating expertise to solve a problem. Prior to Aristotle, the DOD had no coordinated mechanism for identifying expertise across 200,000 of its employees. Dr. Alok Das, the senior scientist for design innovation tasked with implementing the system, explained, “We don’t know what we know.”
This is a common situation. The government currently has no systematic way of getting help from all those with relevant expertise, experience, and passion. For every success on Challenge.gov—the federal government’s platform where agencies post open calls to solve problems for a prize—there are a dozen open-call projects that never get seen by those who might have the insight or experience to help. This kind of crowdsourcing is still too ad hoc, infrequent, and unpredictable—in short, too unreliable—for the purposes of policy-making.
Which is why technologies like Aristotle are so exciting. Smart, searchable expert networks offer the potential to lower the costs and speed up the process of finding relevant expertise. Aristotle never reached this stage, but an ideal expert network is a directory capable of including not just experts within the government, but also outside citizens with specialized knowledge. This leads to a dual benefit: accelerating the path to innovative and effective solutions to hard problems while at the same time fostering greater citizen engagement.
Could such an expert-network platform revitalize the regulatory-review process? We might find out soon enough, thanks to the Food and Drug Administration…”
Bridging Distant Worlds: Innovation in the Civic Space
A digital white paper by Public Innovation: “In an increasingly complex world, today’s challenges are interconnected. Many have argued that our civic institutions are not equipped to respond with the same velocity at which technology is advancing other sectors of the economy. While this may, in fact, be a fair criticism of our electoral, fiscal, and policy structures, a new mindset is emerging at government’s service delivery layer.
Civic innovation offers a new approach to solving community problems that is emergent, generative, resilient, participatory, human-centered, and driven by a process of validated learning where core assumptions are tested quickly and iteratively – and lead to better solutions that are both impactful and durable. And perhaps most surprisingly, new markets are being created that enable creative problem solvers to sustain their social impact through activities that don’t rely on traditional models of grant funding.
While the Sacramento region is making significant progress in this space, our civic innovation and entrepreneurship ecosystem has yet to reach its full potential. The purpose of this white paper is to make the case for why now is the time for a Regional Civic Technology, Innovation and Entrepreneurship Agenda.
The paper concludes with a set of recommendations for collective action among the region’s public, private, nonprofit organizations, and, of course, our fellow citizens. Appendix A articulates this agenda in the form of a resolution to be adopted by as many cities and counties the region as possible.
A recurring theme in this paper is that technology is fundamentally changing the way humans interact with organizations and each other. In order for regional leaders and residents to be honest with ourselves, we must consciously choose whether or not we are going to raise our expectations and co-create a new civic experience.
Because the future is now and the opportunities are infinite…”
Agency Liability Stemming from Citizen-Generated Data
Paper by Bailey Smith for The Wilson Center’s Science and Technology Innovation Program: “New ways to gather data are on the rise. One of these ways is through citizen science. According to a new paper by Bailey Smith, JD, federal agencies can feel confident about using citizen science for a few reasons. First, the legal system provides significant protection from liability through the Federal Torts Claim Act (FTCA) and Administrative Procedures Act (APA). Second, training and technological innovation has made it easier for the non-scientist to collect high quality data.”
When Big Data Maps Your Safest, Shortest Walk Home
Sarah Laskow at NextCity: “Boston University and University of Pittsburgh researchers are trying to do the same thing that got the creators of the app SketchFactor into so much trouble over the summer. They’re trying to show people how to avoid dangerous spots on city streets while walking from one place to another.
“What we are interested in is finding paths that offer trade-offs between safety and distance,” Esther Galbrun, a postdoc at Boston University, recently said in New York at the 3rd International Workshop on Urban Computing, held in conjunction with KDD2014.
She was presenting, “Safe Navigation in Urban Environments,” which describes a set of algorithms that would give a person walking through a city options for getting from one place to another — the shortest path, the safest path and a number of alternatives that balanced between both factors. The paper takes existing algorithms, well defined in theory — nothing new or fancy, Galbrun says — and applies them to a problem that people face everyday.
Imagine, she suggests, that a person is standing at the Philadelphia Museum of Art, and he wants to walk home, to his place on Wharton Street. (Galbrun and her colleagues looked at Philadelphia and Chicago because those cities have made their crime data openly available.) The walk is about three miles away, and one option would be to take the shortest path back. But maybe he’s worried about safety. Maybe he’s willing to take a little bit of a longer walk if it means he has to worry less about crime. What route should he take then?
Services like Google Maps have excelled at finding the shortest, most direct routes from Point A to Point B. But, increasingly, urban computing is looking to capture other aspects of moving about a place. “Fast is only one option,” says co-author Konstantinos Pelechrinis. “There are noble objectives beyond the surface path that you can put inside this navigation problem.” You might look for the path that will burn the most calories; a Yahoo! lab has considered how to send people along the most scenic route.
But working on routes that do more than give simple directions can have its pitfalls. The SketchFactor app relies both on crime data, when it’s available, and crowdsourced comments to reveal potential trouble spots to users. When it was released this summer, tech reporters and other critics immediately started talking about how it could easily become a conduit for racism. (“Sketchy” is, after all, a very subjective measure.)
So far, though, the problem with the SketchFactor app is less that it offers racially skewed perspectives than that the information it does offer is pretty useless — if entertaining. A pinpoint marked “very sketchy” is just as likely to flag an incident like a Jewish man eating pork products or hipster kids making too much noise as it is to flag a mugging.
Here, then, is a clear example of how Big Data has an advantage over Big Anecdata. The SafePath set-up measures risk more objectively and elegantly. It pulls in openly available crime data and considers simple data like time, location and types of crime. While a crime occurs at a discrete point, the researchers wanted to estimate the risk of a crime on every street, at every point. So they use a mathematical tool that smooths out the crime data over the space of the city and allows them to measure the relative risk of witnessing a crime on every street segment in a city….”
The Decalogue of Policy Making 2.0: Results from Analysis of Case Studies on the Impact of ICT for Governance and Policy Modelling
Paper by Sotirios Koussouris, Fenareti Lampathaki, Gianluca Misuraca, Panagiotis Kokkinakos, and Dimitrios Askounis: “Despite the availability of a myriad of Information and Communication Technologies (ICT) based tools and methodologies for supporting governance and the formulation of policies, including modelling expected impacts, these have proved to be unable to cope with the dire challenges of the contemporary society. In this chapter we present the results of the analysis of a set of promising cases researched in order to understand the possible impact of what we define ‘Policy Making 2.0’, which refers to ‘a set of methodologies and technological solutions aimed at enabling better, timely and participative policy-making’. Based on the analysis of these cases we suggest a bouquet of (mostly ICT-related) practical and research recommendations that are relevant to researchers, practitioners and policy makers in order to guide the introduction and implementation of Policy Making 2.0 initiatives. We argue that this ‘decalogue’ of Policy Making 2.0 could be an operational checklist for future research and policy to further explore the potential of ICT tools for governance and policy modelling, so to make next generation policy making more ‘intelligent’ and hopefully able to solve or anticipate the societal challenges we are (and will be) confronted today and in the future.
Using Crowds for Evaluation Tasks: Validity by Numbers vs. Validity by Expertise
Paper by Christoph Hienerth and Frederik Riar: “Developing and commercializing novel ideas is central to innovation processes. As the outcome of such ideas cannot fully be foreseen, the evaluation of them is crucial. With the rise of the internet and ICT, more and new kinds of evaluations are done by crowds. This raises the question whether individuals in crowds possess necessary capabilities to evaluate and whether their outcomes are valid. As empirical insights are not yet available, this paper deals with the examination of evaluation processes and general evaluation components, the discussion of underlying characteristics and mechanism of these components affecting evaluation outcomes (i.e. evaluation validity). We further investigate differences between firm- and crowd-based evaluation using different cases of applications, and develop a theoretical framework towards evaluation validity, i.e. validity by numbers vs. the validity by expertise. The identified factors that influence the validity of evaluations are: (1) the number of evaluation tasks, (2) complexity, (3) expertise, (4) costs, and (5) time to outcome. For each of these factors, hypotheses are developed based on theoretical arguments. We conclude with implications, proposing a model of evaluation validity.”
A Few Useful Things to Know about Machine Learning
A new research paper by Pedro Domingos: “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.”
Follow the money: A study of cashtags on Twitter
Paper by Martin Hentschel and Omar Alonso at FirstMonday: “The popularity of Twitter goes beyond trending topics, world events, memes, and popular hashtags. Recently a new way of sharing financial information is taking place in social media under the name of cashtags, stock ticker symbols that are prefixed with a dollar sign. In this paper we present an exploratory analysis of cashtags on Twitter. Specifically, we investigate how widespread cashtags are, what stock symbols are tweeted more often, and which users tweet about cashtags in general. We analyze relationships among cashtags and study hashtags in the context of cashtags. Finally, we compare tweet performance to stock market performance. We conclude that cashtags, in particular in combination with other cashtags or hashtags, can be very useful for analyzing financial information and provide new insights into stocks and companies.”
Behavior Analysis in Social Media
Paper by Reza Zafarani and Huan Liu in IEEE Intelligent Systems (Volume 29, Issue 4, 2014): “With the rise of social media, information sharing has been democratized. As a result, users are given opportunities to exhibit different behaviors such as sharing, posting, liking, commenting, and befriending conveniently and on a daily basis. By analyzing behaviors observed on social media, we can categorize these behaviors into individual and collective behavior. Individual behavior is exhibited by a single user, whereas collective behavior is observed when a group of users behave together. For instance, users using the same hashtag on Twitter or migrating to another social media site are examples of collective behavior. User activities on social media generate behavioral data, which is massive, expansive, and indicative of user preferences, interests, opinions, and relationships. This behavioral data provides a new lens through which we can observe and analyze individual and collective behaviors of users.”
Federalism and Municipal Innovation: Lessons from the Fight Against Vacant Properties
New Paper by Benton Martin: “Cities possess a far greater ability to be trailblazers on a national scale than local officials may imagine. Realizing this, city advocates continue to call for renewed recognition by state and federal officials of the benefits of creative local problem-solving. The goal is admirable but warrants caution. The key to successful local initiatives lies not in woolgathering about cooperation with other levels of government but in identifying potential conflicts and using hard work and political savvy to build constituencies and head off objections. To demonstrate that point, this Article examines the legal status of local governments and recent efforts to regulate vacant property through land banking and registration ordinances.”