The Unsung Role That Ordinary Citizens Played in the Great Crime Decline

Curated on Posted on by Stefaan Verhulst

Emily Badger in The New York Times: “Most theories for the great crime decline that swept across nearly every major American city over the last 25 years have focused on the would-be criminals.

Their lives changed in many ways starting in the 1990s: Strict new policing tactics kept closer watch on them. Mass incarceration locked them up in growing numbers. The crack epidemic that ensnared many began to recede. Even the more unorthodox theories — around the rise of abortion, the reduction in lead or the spread of A.D.H.D. medication — have argued that larger shifts in society altered the behavior (and existence) of potential criminals.

But none of these explanations have paid much attention to the communities where violence plummeted the most. New research suggests that people there were working hard, with little credit, to address the problem themselves.

Local nonprofit groups that responded to the violence by cleaning streets, building playgrounds, mentoring children and employing young men had a real effect on the crime rate. That’s what Patrick Sharkey, a sociologist at New York University, argues in a new study and a forthcoming book. Mr. Sharkey doesn’t contend that community groups alone drove the national decline in crime, but rather that their impact is a major missing piece.

“This was a part that has been completely overlooked and ignored in national debates over the crime drop,” he said. “But I think it’s fundamental to what happened.”…(More)”.

Participatory Budgeting: Does Evidence Match Enthusiasm?

Curated on Posted on by Stefaan Verhulst

Brian Wampler, Stephanie McNulty, and Michael Touchton at Open Government Partnership: “Participatory budgeting (PB) empowers citizens to allocate portions of public budgets in a way that best fits the needs of the people. In turn, proponents expect PB to improve citizens’ lives in important ways, by expanding their participation in politics, providing better public services such as in healthcare, sanitation, or education, and giving them a sense of efficacy.

Below we outline several potential outcomes that emerge from PB. Of course, assessing PB’s potential impact is difficult, because reliable data is rare and PB is often one of several programs that could generate similar improvements at the same time. Impact evaluations for PB are thus at a very early stage. Nevertheless, considerable case study evidence and some broader, comparative studies point to outcomes in the following areas:

Citizens’ attitudes: Early research focused on the attitudes of citizens who participate in PB, and found that PB participants feel empowered, support democracy, view the government as more effective, and better understand budget and government processes after participating (Wampler and Avritzer 2004; Baiocchi 2005; Wampler 2007).

Participants’ behavior: Case-study evidence shows that PB participants increase their political participation beyond PB and join civil society groups. Many scholars also expect PB to strengthen civil society by increasing its density (number of groups), expanding its range of activities, and brokering new partnerships with government and other CSOs. There is some case study evidence that this occurs (Baiocchi 2005; McNulty 2011; Baiocchi, Heller and Silva 2011; Van Cott 2008) as well as evidence from over 100 PB programs across Brazil’s larger municipalities (Touchton and Wampler 2014). Proponents also expect PB to educate government officials surrounding community needs, to increase their support for participatory processes, and to potentially expand participatory processes in complementary areas. Early reports from five counties in Kenya suggest that PB ther is producing at least some of these impacts.

Electoral politics and governance: PB can also promote social change, which may alter local political calculations and the ways that governments operate. PB may deliver votes to the elected officials that sponsor it, improve budget transparency and resource allocation, decrease waste and fraud, and generally improve accountability. However, there is very little evidence in this area because few studies have been able to measure these impacts in any direct way.

Social well-being: Finally, PB is designed to improve residents’ well-being. Implemented PB projects include funding for healthcare centers, sewage lines, schools, wells, and other areas that contribute directly to well-being. These effects may take years to appear, but recent studies attribute improvements in infant mortality in Brazil to PB (Touchton and Wampler 2014; Gonçalves 2014). Beyond infant mortality, the range of potential impacts extends to other health areas, sanitation, education, and poverty in general. We are cautious here because results from Brazil might not appear elsewhere: what works in urban Brazil might not in rural Indonesia….(More)”.

Bot.Me: A revolutionary partnership

Curated on Posted on by Stefaan Verhulst

PWC Consumer Intelligence Series: “The modern world has been shaped by the technological revolutions of the past, like the Industrial Revolution and the Information Revolution. The former redefined the way the world values both human and material resources; the latter redefined value in terms of resources while democratizing information. Today, as technology progresses even further, value is certain to shift again, with a focus on sentiments more intrinsic to the human experience: thinking, creativity, and problem-solving. AI, shorthand for artificial intelligence, defines technologies emerging today that can understand, learn, and then act based on that information. Forms of AI in use today include digital assistants, chatbots, and machine learning.

Today, AI works in three ways:

  • Assisted intelligence, widely available today, improves what people and organizations are already doing. A simple example, prevalent in cars today, is the GPS navigation program that offers directions to drivers and adjusts to road conditions.
  • Augmented intelligence, emerging today, enables people and organizations to do things they couldn’t otherwise do. For example, the combination of programs that organize cars in ride-sharing services enables businesses that could not otherwise exist.
  • Autonomous intelligence, being developed for the future, establishes machines that act on their own. An example of this will be self-driving vehicles, when they come into widespread use.

With a market projected to reach $70 billion by 2020, AI is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. Some tech optimists believe AI could create a world where human abilities are amplified as machines help mankind process, analyze, and evaluate the abundance of data that creates today’s world, allowing humans to spend more time engaged in high-level thinking, creativity, and decision-making. Technological revolutions, like the Industrial Revolution and the Information Revolution, didn’t happen overnight. In fact, people in the midst of those revolutions often didn’t even realize they were happening, until history was recorded later.

That is where we find ourselves today, in the very beginning of what some are calling the “augmented age.” Just like humans in the past, it is up to mankind to find the best ways to leverage these machine revolutions to help the world evolve. As Isaac Asimov, the prolific science fiction writer with many works on AI mused, “No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be.” As a future with AI approaches, it’s important to understand how people think of it today, how it will amplify the world tomorrow, and what guiding principles will be required to navigate this monumental change….(More)”.

Augmented CI and Human-Driven AI: How the Intersection of Artificial Intelligence and Collective Intelligence Could Enhance Their Impact on Society

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan Verhulst: “As the technology, research and policy communities continue to seek new ways to improve governance and solve public problems, two new types of assets are occupying increasing importance: data and people. Leveraging data and people’s expertise in new ways offers a path forward for smarter decisions, more innovative policymaking, and more accountability in governance. Yet, unlocking the value of these two assets not only requires increased availability and accessibility (through, for instance, open data or open innovation), it also requires innovation in methodology and technology.

The first of these innovations involves Artificial Intelligence (AI). AI offers unprecedented abilities to quickly process vast quantities of data that can provide data-driven insights to address public needs. This is the role it has for example played in New York City, where FireCast, leverages data from across the city government to help the Fire Department identify buildings with the highest fire risks. AI is also considered to improve education, urban transportation,  humanitarian aid and combat corruption, among other sectors and challenges.

The second area is Collective Intelligence (CI). Although it receives less attention than AI, CI offers similar potential breakthroughs in changing how we govern, primarily by creating a means for tapping into the “wisdom of the crowd” and allowing groups to create better solutions than even the smartest experts working in isolation could ever hope to achieve. For example, in several countries patients’ groups are coming together to create new knowledge and health treatments based on their experiences and accumulated expertise. Similarly, scientists are engaging citizens in new ways to tap into their expertise or skills, generating citizen science – ranging from mapping our solar system to manipulating enzyme models in a game-like fashion.

Neither AI nor CI offer panaceas for all our ills; they each pose certain challenges, and even risks.  The effectiveness and accuracy of AI relies substantially on the quality of the underlying data as well as the human-designed algorithms used to analyse that data. Among other challenges, it is becoming increasingly clear how biases against minorities and other vulnerable populations can be built into these algorithms. For instance, some AI-driven platforms for predicting criminal recidivism significantly over-estimate the likelihood that black defendants will commit additional crimes in comparison to white counterparts. (for more examples, see our reading list on algorithmic scrutiny).

In theory, CI avoids some of the risks of bias and exclusion because it is specifically designed to bring more voices into a conversation. But ensuring that that multiplicity of voices adds value, not just noise, can be an operational and ethical challenge. As it stands, identifying the signal in the noise in CI initiatives can be time-consuming and resource intensive, especially for smaller organizations or groups lacking resources or technical skills.

Despite these challenges, however, there exists a significant degree of optimism  surrounding both these new approaches to problem solving. Some of this is hype, but some of it is merited—CI and AI do offer very real potential, and the task facing both policymakers, practitioners and researchers is to find ways of harnessing that potential in a way that maximizes benefits while limiting possible harms.

In what follows, I argue that the solution to the challenge described above may involve a greater interaction between AI and CI. These two areas of innovation have largely evolved and been researched separately until now. However, I believe that there is substantial scope for integration, and mutual reinforcement. It is when harnessed together, as complementary methods and approaches, that AI and CI can bring the full weight of technological progress and modern data analytics to bear on our most complex, pressing problems.

To deconstruct that statement, I propose three premises (and subsequent set of research questions) toward establishing a necessary research agenda on the intersection of AI and CI that can build more inclusive and effective approaches to governance innovation.

Premise I: Toward Augmented Collective Intelligence: AI will enable CI to scale

Premise II: Toward Human-Driven Artificial Intelligence: CI will humanize AI

Premise III: Open Governance will drive a blurring between AI and CI

…(More)”.

Order Without Intellectual Property Law: Open Science in Influenza

Curated on Posted on by Stefaan Verhulst

Amy Kapczynski at Cornell Law Review: “Today, intellectual property (IP) scholars accept that IP as an approach to information production has serious limits. But what lies beyond IP? A new literature on “intellectual production without IP” (or “IP without IP”) has emerged to explore this question, but its examples and explanations have yet to convince skeptics.

This Article reorients this new literature via a study of a hard case: a global influenza virus-sharing network that has for decades produced critically important information goods, at significant expense, and in a loose-knit group — all without recourse to IP. I analyze the Network as an example of “open science,” a mode of information production that differs strikingly from conventional IP, and yet that successfully produces important scientific goods in response to social need.

The theory and example developed here refute the most powerful criticisms of the emerging “IP without IP” literature, and provide a stronger foundation for this important new field. Even where capital costs are high, creation without IP can be reasonably effective in social terms, if it can link sources of funding to reputational and evaluative feedback loops like those that characterize open science. It can also be sustained over time, even by loose-knit groups and where the stakes are high, because organizations and other forms of law can help to stabilize cooperation. I also show that contract law is well suited to modes of information production that rely upon a “supply side” rather than “demand side” model. In its most important instances, “order without IP” is not order without governance, nor order without law. Recognizing this can help us better ground this new field, and better study and support forms of knowledge production that deserve our attention, and that sometimes sustain our very lives….(More)”.

Democracy Needs a Reboot for the Age of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Katharine Dempsey at The Nation: “…A healthy modern democracy requires ordinary citizens to participate in public discussions about rapidly advancing technologies. We desperately need new policies, regulations, and safety nets for those displaced by machines. With computing power accelerating exponentially, the scale of AI’s significance is still not being fully internalized. The 2017 McKinsey Global Initiative report “A Future that Works” predicts that AI and advanced robotics could automate roughly half of all work globally by 2055, but, McKinsey notes, “this could happen up to 20 years earlier or later depending on the various factors, in addition to other wider economic conditions.”

Granted, the media are producing more articles focused on artificial intelligence, but too often these pieces veer into hysterics. Wired magazine labeled this year’s coverage “The Great Tech Panic of 2017.” We need less fear-mongering and more rational conversation. Dystopian narratives, while entertaining, can also be disorienting. Skynet from the Terminatormovies is not imminent. But that doesn’t mean there aren’t hazards ahead….

Increasingly, to thoughtfully discuss ethics, politics, or business, the general population needs to pay attention to AI. In 1989, Ursula Franklin, the distinguished German-Canadian experimental physicist, delivered a series of lectures titled “The Real World of Technology.” Franklin opened her lectures with an important observation: “The viability of technology, like democracy, depends in the end on the practice of justice and on the enforcements of limits to power.”

For Franklin, technology is not a neutral set of tools; it can’t be divorced from society or values. Franklin further warned that “prescriptive technologies”—ones that isolate tasks, such as factory-style work—find their way into our social infrastructures and create modes of compliance and orthodoxy. These technologies facilitate top-down control….(More)”.

Once Upon an Algorithm: How Stories Explain Computing

Curated on Posted on by Stefaan Verhulst

Book by Martin Erwig: “Picture a computer scientist, staring at a screen and clicking away frantically on a keyboard, hacking into a system, or perhaps developing an app. Now delete that picture. In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm.

Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie Groundhog Day illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter’s world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; “intractable” problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms.

This engaging book explains computation accessibly and shows its relevance to daily life. Something to think about next time we execute the algorithm of getting up in the morning…(More)”.

Randomized Controlled Trials: How Can We Know ‘What Works’?

Curated on Posted on by Stefaan Verhulst

Nick Cowen et al at Critical Review: “We attempt to map the limits of evidence-based policy through an interactive theoretical critique and empirical case-study. We outline the emergence of an experimental turn in EBP among British policymakers and the limited, broadly inductive, epistemic approach that underlies it. We see whether and how field professionals identify and react to these limitations through a case study of teaching professionals subject to a push to integrate research evidence into their practice. Results suggest that many of the challenges of establishing evidential warrant that EBP is supposed to streamline re-appear at the level of choice of locally effective policies and implementation…(More)”.

Most of the public doesn’t know what open data is or how to use it

Curated on Posted on by Stefaan Verhulst

Jason Shueh at Statescoop: “New survey results show that despite the aggressive growth of open data, there is a drastic need for greater awareness and accessibility.

Results of a global survey published last month by Singapore’s Government Technology agency (GovTech) and the Economist Intelligence Unit, a British forecasting and advisory firm, show that open data is not being utilized as effectively as it could be. Researchers surveyed more than 1,000 residents in the U.S. and nine other leading open data counties and found that “an overwhelming” number of respondents say the primary barrier to open data’s use and effectiveness is a lack of public awareness.

The study reports that 50 percent of respondents said that national and local governments need to expand their civic engagements efforts on open data.

“Half of respondents say there is not enough awareness in their country about open government data initiatives and their benefits or potential uses,” the reports notes. “This is seen as the biggest barrier to more open government data use, particularly by citizens in India and Mexico.”

Accessibility is named as the second largest hurdle, with 31 percent calling for more relevant data. Twenty-five percent say open data is difficult to use due to a lack of standardized formats and another 25 percent say they don’t have the skills to understand open data.

Those calling for more relevant data say they wanted to see more information on crime, the economy and the environment, yet report they are happy with the availability and use of open data related to transportation….

When asked to name the main benefit of open data, 70 percent say greater transparency, 78 percent say to drive a better quality of life, and 53 percent cite better decision making….(More)”.

Crowdsourced Smart Cities

Curated on Posted on by Stefaan Verhulst

Paper by Robert A Iannucci and Anthony Rowe: “The vision of applying computing and communication technologies to enhance life in our cities is fundamentally appealing. Pervasive sensing and computing can alert us to imminent dangers, particularly with respect to the movement of vehicles and pedestrians in and around crowded streets. Signaling systems can integrate knowledge of city-scale traffic congestion. Self-driving vehicles can borrow from and contribute to a city-scale information collaborative. Achieving this vision will require significant coordination among the creators of sensors, actuators, and application-level software systems. Cities will invest in such smart infrastructure if and only if they are convinced that the value can be realized. Investment by technology providers in creation of the infrastructure depends to a large degree on their belief in a broad and ready market. To accelerate innovation, this stalemate must be broken. Borrowing a page from the evolution of the internet, we put forward the notion that an initially minimalist networking infrastructure that is well suited to smart city concepts can break this cycle and empower co-development of both clever city-sensing devices and valuable city-scale applications, with players large and small being empowered in the process. We call this the crowdsourced smart city concept. We illustrate the concept via an examination of our ongoing project to crowdsource real-time traffic data, arguing that this can rapidly generalize to many more smart city applications. This exploration motivates study of a number of smart city challenges, crowdsourced or otherwise, leading to a paradigm shift we call edgeless computing….(More)”.