Chapter by William Bowen and Robert Gleeson in The Evolution of Human Settlements: “…argue that the evolutionary processes by which human settlements have evolved through countless experiments throughout millennia are the most likely paths for resolving today’s greatest problems. Darwin’s great insight has important implications for understanding decision-making, the direction of change and the governance of complex, large-scale settlement systems. Darwinian views accommodate fallible Homo sapiens making decisions, some of which work and others that do not. Darwinian views imply the value of diverse institutions and reliance upon general patterns of social, ideational, and technical interaction rather than upon specific policies designed to directly produce particular results for particular individuals, groups, and settlement systems. Solutions will evolve only if we ensure continuous, diverse, problem-solving initiatives….(More).
#TrendingLaws: How can Machine Learning and Network Analysis help us identify the “influencers” of Constitutions?
Unicef: “New research by scientists from UNICEF’s Office of Innovation — published today in the journal Nature Human Behaviour — applies methods from network science and machine learning to constitutional law. UNICEF Innovation Data Scientists Alex Rutherford and Manuel Garcia-Herranz collaborated with computer scientists and political scientists at MIT, George Washington University, and UC Merced to apply data analysis to the world’s constitutions over the last 300 years. This work sheds new light on how to better understand why countries’ laws change and incorporate social rights…
Data science techniques allow us to use methods like network science and machine learning to uncover patterns and insights that are hard for humans to see. Just as we can map influential users on Twitter — and patterns of relations between places to predict how diseases will spread — we can identify which countries have influenced each other in the past and what are the relations between legal provisions.
One way UNICEF fulfills its mission is through advocacy with national governments — to enshrine rights for minorities, notably children, formally in law. Perhaps the most renowned example of this is the International Convention on the Rights of the Child (ICRC).
Constitutions, such as Mexico’s 1917 constitution — the first to limit the employment of children — are critical to formalizing rights for vulnerable populations. National constitutions describe the role of a country’s institutions, its character in the eyes of the world, as well as the rights of its citizens.
From a scientific standpoint, the work is an important first step in showing that network analysis and machine learning technique can be used to better understand the dynamics of caring for and protecting the rights of children — critical to the work we do in a complex and interconnected world. It shows the significant, and positive policy implications of using data science to uphold children’s rights.
What the Research Shows:Through this research, we uncovered:
- A network of relationships between countries and their constitutions.
- A natural progression of laws — where fundamental rights are a necessary precursor to more specific rights for minorities.
- The effect of key historical events in changing legal norms….(More)”.
Satellites can advance sustainable development by highlighting poverty
Cordis: “Estimating poverty is crucial for improving policymaking and advancing the sustainability of a society. Traditional poverty estimation methods such as household surveys and census data incur huge costs however, creating a need for more efficient approaches.
With this in mind, the EU-funded USES project examined how satellite images could be used to estimate household-level poverty in rural regions of developing countries. “This promises to be a radically more cost-effective way of monitoring and evaluating the Sustainable Development Goals,” says Dr Gary Watmough, USES collaborator and Interdisciplinary Lecturer in Land Use and Socioecological Systems at the University of Edinburgh, United Kingdom.
Land use and land cover reveal poverty clues
To achieve its aims, the project investigated how land use and land cover information from satellite data could be linked with household survey data. “We looked particularly at how households use the landscape in the local area for agriculture and other purposes such as collecting firewood and using open areas for grazing cattle,” explains Dr Watmough.
The work also involved examining satellite images to determine which types of land use were related to household wealth or poverty using statistical analysis. “By trying to predict household poverty using the land use data we could see which land use variables were most related to the household wealth in the area,” adds Dr Watmough.
Overall, the USES project found that satellite data could predict poverty particularly the poorest households in the area. Dr Watmough comments: “This is quite remarkable given that we are trying to predict complicated household-level poverty from a simple land use map derived from high-resolution satellite data.”
A study conducted by USES in Kenya found that the most important remotely sensed variable was building size within the homestead. Buildings less than 140 m2 were mostly associated with poorer households, whereas those over 140 m2 tended to be wealthier. The amount of bare ground in agricultural fields and within the homestead region was also important. “We also found that poorer households were associated with a shorter number of agricultural growing days,” says Dr Watmough….(More)”.
Technology, Activism, and Social Justice in a Digital Age
Book edited by John G. McNutt: “…offers a close look at both the present nature and future prospects for social change. In particular, the text explores the cutting edge of technology and social change, while discussing developments in social media, civic technology, and leaderless organizations — as well as more traditional approaches to social change.
It effectively assembles a rich variety of perspectives to the issue of technology and social change; the featured authors are academics and practitioners (representing both new voices and experienced researchers) who share a common devotion to a future that is just, fair, and supportive of human potential.
They come from the fields of social work, public administration, journalism, law, philanthropy, urban affairs, planning, and education, and their work builds upon 30-plus years of research. The authors’ efforts to examine changing nature of social change organizations and the issues they face will help readers reflect upon modern advocacy, social change, and the potential to utilize technology in making a difference….(More)”
To Better Predict Traffic, Look to the Electric Grid
Linda Poon at CityLab: “The way we consume power after midnight can reveal how we bad the morning rush hour will be….
But “the information we extract from traffic data has been exhausted,” said Zhen (Sean) Qian, who directs the Mobility Data Analytics Center at Carnegie Mellon University. He thinks that to more accurately predict how gridlock varies from day to day, there’s a whole other set of data that cities haven’t mined yet: electricity use.
“Essentially we all use the urban system—the electricity, water, the sewage system and gas—and when people use them and how heavily they do is correlated to the way they use the transportation system,” he said. How we use electricity at night, it turns out, can reveal when we leave for work the next day. “So we might be able to get new information that helps explain travel time one or two hours in advance by having a better understanding of human activity.”
In a recent study in the journal Transportation Research Part C, Qian and his student Pinchao Zhang used 2014 data to demonstrate how electricity usage patterns can predict when peak congestion begins on various segments of a major highway in Austin, Texas—the 14th most congested city in the U.S. They crunched 79 days worth of electricity usage data for 322 households (stripped of all private information, including location), feeding it into a machine learning algorithm that then categorized the households into 10 groups according to the time and amount of electricity use between midnight and 6 a.m. By extrapolating the most critical traffic-related information about each group for each day, the model then predicted what the commute may look like that morning.
What’s Wrong with Public Policy Education
Francis Fukuyama at the American Interest: “Most programs train students to become capable policy analysts, but with no understanding of how to implement those policies in the real world…Public policy education is ripe for an overhaul…
Public policy education in most American universities today reflects a broader problem in the social sciences, which is the dominance of economics. Most programs center on teaching students a battery of quantitative methods that are useful in policy analysis: applied econometrics, cost-benefit analysis, decision analysis, and, most recently, use of randomized experiments for program evaluation. Many schools build their curricula around these methods rather than the substantive areas of policy such as health, education, defense, criminal justice, or foreign policy. Students come out of these programs qualified to be policy analysts: They know how to gather data, analyze it rigorously, and evaluate the effectiveness of different public policy interventions. Historically, this approach started with the Rand Graduate School in the 1970s (which has subsequently undergone a major re-thinking of its approach).
There is no question that these skills are valuable and should be part of a public policy education. The world has undergone a revolution in recent decades in terms of the role of evidence-based policy analysis, where policymakers can rely not just on anecdotes and seat-of-the-pants assessments, but statistically valid inferences that intervention X is likely to result in outcome Y, or that the millions of dollars spent on policy Z has actually had no measurable impact. Evidence-based policymaking is particularly necessary in the age of Donald Trump, amid the broad denigration of inconvenient facts that do not suit politicians’ prior preferences.
But being skilled in policy analysis is woefully inadequate to bring about policy change in the real world. Policy analysis will tell you what the optimal policy should be, but it does not tell you how to achieve that outcome.
The world is littered with optimal policies that don’t have a snowball’s chance in hell of being adopted. Take for example a carbon tax, which a wide range of economists and policy analysts will tell you is the most efficient way to abate carbon emissions, reduce fossil fuel dependence, and achieve a host of other desired objectives. A carbon tax has been a nonstarter for years due to the protestations of a range of interest groups, from oil and chemical companies to truckers and cabbies and ordinary drivers who do not want to pay more for the gas they use to commute to work, or as inputs to their industrial processes. Implementing a carbon tax would require a complex strategy bringing together a coalition of groups that are willing to support it, figuring out how to neutralize the die-hard opponents, and convincing those on the fence that the policy would be a good, or at least a tolerable, thing. How to organize such a coalition, how to communicate a winning message, and how to manage the politics on a state and federal level would all be part of a necessary implementation strategy.
It is entirely possible that an analysis of the implementation strategy, rather than analysis of the underlying policy, will tell you that the goal is unachievable absent an external shock, which might then mean changing the scope of the policy, rethinking its objectives, or even deciding that you are pursuing the wrong objective.
Public policy education that sought to produce change-makers rather than policy analysts would therefore have to be different. It would continue to teach policy analysis, but the latter would be a small component embedded in a broader set of skills.
The first set of skills would involve problem definition. A change-maker needs to query stakeholders about what they see as the policy problem, understand the local history, culture, and political system, and define a problem that is sufficiently narrow in scope that it can plausibly be solved.
At times reformers start with a favored solution without defining the right problem. A student I know spent a summer working at an NGO in India advocating use of electric cars in the interest of carbon abatement. It turns out, however, that India’s reliance on coal for marginal electricity generation means that more carbon would be put in the air if the country were to switch to electric vehicles, not less, so the group was actually contributing to the problem they were trying to solve….
The second set of skills concerns solutions development. This is where traditional policy analysis comes in: It is important to generate data, come up with a theory of change, and posit plausible options by which reformers can solve the problem they have set for themselves. This is where some ideas from product design, like rapid prototyping and testing, may be relevant.
The third and perhaps most important set of skills has to do with implementation. This begins necessarily with stakeholder analysis: that is, mapping of actors who are concerned with the particular policy problem, either as supporters of a solution, or opponents who want to maintain the status quo. From an analysis of the power and interests of the different stakeholders, one can begin to build coalitions of proponents, and think about strategies for expanding the coalition and neutralizing those who are opposed. A reformer needs to think about where resources can be obtained, and, very critically, how to communicate one’s goals to the stakeholder audiences involved. Finally comes testing and evaluation—not in the expectation that there will be a continuous and rapid iterative process by which solutions are tried, evaluated, and modified. Randomized experiments have become the gold standard for program evaluation in recent years, but their cost and length of time to completion are often the enemies of rapid iteration and experimentation….(More) (see also http://canvas.govlabacademy.org/).
Open Data Use Case: Using data to improve public health
Chris Willsher at ODX: “Studies have shown that a large majority of Canadians spend too much time in sedentary activities. According to the Health Status of Canadians report in 2016, only 2 out of 10 Canadian adults met the Canadian Physical Activity Guidelines. Increasing physical activity and healthy lifestyle behaviours can reduce the risk of chronic illnesses, which can decrease pressures on our health care system. And data can play a role in improving public health.
We are already seeing examples of a push to augment the role of data, with programs recently being launched at home and abroad. Canada and the US established an initiative in the spring of 2017 called the Healthy Behaviour Data Challenge. The goal of the initiative is to open up new methods for generating and using data to monitor health, specifically in the areas of physical activity, sleep, sedentary behaviour, or nutrition. The challenge recently wrapped up with winners being announced in late April 2018. Programs such as this provide incentive to the private sector to explore data’s role in measuring healthy lifestyles and raise awareness of the importance of finding new solutions.
In the UK, Sport England and the Open Data Institute (ODI) have collaborated to create the OpenActive initiative. It has set out to encourage both government and private sector entities to unlock data around physical activities so that others can utilize this information to ease the process of engaging in an active lifestyle. The goal is to “make it as easy to find and book a badminton court as it is to book a hotel room.” As of last fall, OpenActive counted more than 76,000 activities across 1,000 locations from their partner organizations. They have also developed a standard for activity data to ensure consistency among data sources, which eases the ability for developers to work with the data. Again, this initiative serves as a mechanism for open data to help address public health issues.
In Canada, we are seeing more open datasets that could be utilized to devise new solutions for generating higher rates of physical activity. A lot of useful information is available at the municipal level that can provide specifics around local infrastructure. Plus, there is data at the provincial and federal level that can provide higher-level insights useful to developing methods for promoting healthier lifestyles.
Information about cycling infrastructure seems to be relatively widespread among municipalities with a robust open data platform. As an example, the City of Toronto, publishes map data of bicycle routes around the city. This information could be utilized in a way to help citizens find the best bike route between two points. In addition, the city also publishes data on indoor, outdoor, and post and ring bicycle parking facilities that can identify where to securely lock your bike. Exploring data from proprietary sources, such as Strava, could further enhance an application by layering on popular cycling routes or allow users to integrate their personal information. And algorithms could allow for the inclusion of data on comparable driving times, projected health benefits, or savings on automotive maintenance.
The City of Calgary publishes data on park sports surfaces and recreation facilities that could potentially be incorporated into sports league applications. This would make it easier to display locations for upcoming games or to arrange pick-up games. Knowing where there are fields nearby that may be available for a last minute soccer game could be useful in encouraging use of the facilities and generating more physical activity. Again, other data sources, such as weather, could be integrated with this information to provide a planning tool for organizing these activities….(More)”.
Under what conditions is information empowering?
FeedbackLabs: “A 72% increase in students ceasing to abuse drugs. A 57 percentage point jump in vaccination rates. Fourteen percent higher odds of adults quitting smoking. The improvements in outcomes that people can achieve for themselves when armed with information can be striking.
Yet the above examples and many more show that information alone rarely empowers people to make their lives better. Information empowers when social and emotional factors induce people to reinterpret that information, and act on it. In this report, we draw on 44 real-life examples and 168 research papers from 10 fields to develop 7 general principles that seem to underlie information initiatives that successfully empower people. Principles 1, 2, and 3 speak to how information empowers through reinterpretation, and Principles 4 to 7 speak to how we can support that reinterpretation—and get people to act. Based on the 7 principles, we then provide a checklist of questions a team can use to increase the likelihood that their initiative will empower the people they seek to serve.
Throughout, we provide concrete illustrations from a wide range of fields to show how applying these principles in practice has led to substantially better outcomes. We also consider examples with outcomes we might consider to be negative. The 7 principles are broadly applicable to how information empowers people to perceive, make and act on choices—but they are agnostic about whether the outcomes of those choices are positive or negative.
The way that the principles are applied in one context may not always work in another. But from the context-specific evidence summarized in this report we have extrapolated a framework that can be applied more broadly—in both theory and practice, for both funders and implementers. Although many of the in-depth case studies presented stem from the US, the principles are based on a wide range of examples and evidence from around the world. We believe the framework we construct here is powerful and can be applied globally; but it’s also clear that much more remains to be understood, so we hope it also sparks ideas, experimentation, and new discoveries….(More)”.
We Need Transparency in Algorithms, But Too Much Can Backfire
Kartik Hosanagar and Vivian Jair at Harvard Business Review: “In 2013, Stanford professor Clifford Nass faced a student revolt. Nass’s students claimed that those in one section of his technology interface course received higher grades on the final exam than counterparts in another. Unfortunately, they were right: two different teaching assistants had graded the two different sections’ exams, and one had been more lenient than the other. Students with similar answers had ended up with different grades.
Nass, a computer scientist, recognized the unfairness and created a technical fix: a simple statistical model to adjust scores, where students got a certain percentage boost on their final mark when graded by a TA known to give grades that percentage lower than average. In the spirit of openness, Nass sent out emails to the class with a full explanation of his algorithm. Further complaints poured in, some even angrier than before. Where had he gone wrong?…
Kizilcec had in fact tested three levels of transparency: low and medium but also high, where the students got not only a paragraph explaining the grading process but also their raw peer-graded scores and how these were each precisely adjusted by the algorithm to get to a final grade. And this is where the results got more interesting. In the experiment, while medium transparency increased trust significantly, high transparency eroded it completely, to the point where trust levels were either equal to or lower than among students experiencing low transparency.
Making Modern AI Transparent: A Fool’s Errand?
What are businesses to take home from this experiment? It suggests that technical transparency – revealing the source code, inputs, and outputs of the algorithm – can build trust in many situations. But most algorithms in the world today are created and managed by for-profit companies, and many businesses regard their algorithms as highly valuable forms of intellectual property that must remain in a “black box.” Some lawmakers have proposed a compromise, suggesting that the source code be revealed to regulators or auditors in the event of a serious problem, and this adjudicator will assure consumers that the process is fair.
This approach merely shifts the burden of belief from the algorithm itself to the regulators. This may a palatable solution in many arenas: for example, few of us fully understand financial markets, so we trust the SEC to take on oversight. But in a world where decisions large and small, personal and societal, are being handed over to algorithms, this becomes less acceptable.
Another problem with technical transparency is that it makes algorithms vulnerable to gaming. If an instructor releases the complete source code for an algorithm grading student essays, it becomes easy for students to exploit loopholes in the code: maybe, for example, the algorithm seeks evidence that the students have done research by looking for phrases such as “according to published research.” A student might then deliberately use this language at the start of every paragraph in her essay.
But the biggest problem is that modern AI is making source code – transparent or not – less relevant compared with other factors in algorithmic functioning. Specifically, machine learning algorithms – and deep learning algorithms in particular – are usually built on just a few hundred lines of code. The algorithms logic is mostly learned from training data and is rarely reflected in its source code. Which is to say, some of today’s best-performing algorithms are often the most opaque. High transparency might involve getting our heads around reams and reams of data – and then still only being able to guess at what lessons the algorithm has learned from it.
This is where Kizilcec’s work becomes relevant – a way to embrace rather than despair over deep learning’s impenetrability. His work shows that users will not trust black box models, but they don’t need – or even want – extremely high levels of transparency. That means responsible companies need not fret over what percentage of source code to reveal, or how to help users “read” massive datasets. Instead, they should work to provide basic insights on the factors driving algorithmic decisions….(More)”
What top technologies should the next generation know how to use?
Lottie Waters at Devex: “Technology provides some great opportunities for global development, and a promising future. But for the next generation of professionals to succeed, it’s vital they stay up to date with the latest tech, innovations, and tools.
In a recent report produced by Devex in collaboration with the United States Agency for International Development and DAI, some 86 percent of survey respondents believe the technology, skills, and approaches development professionals will be using in 10 years’ time will be significantly different to today’s.
In fact, “technology for development” is regarded as the sector that will see the most development progress, but is also cited as the one that will see the biggest changes in skills required, according to the survey.
“As different technologies develop, new possibilities will open up that we may not even be aware of yet. These opportunities will bring new people into the development sector and require those in it to be more agile in adapting technologies to meet development challenges,” said one survey respondent.
While “blockchain,” “artificial intelligence,” and “drones” may be the current buzzwords surrounding tech in global development, geographical information systems, or GIS, and big data are actually the top technologies respondents believe the next generation of development professionals should learn how to utilize.
So, how are these technologies currently being used in development, how might this change in the near future, and what will their impact be in the next 10 years? Devex spoke with experts in the field who are already integrating these technologies into their work to find out….(More)”