7 lessons learned from $5 million in open innovation prizes

Curated on Posted on by Stefaan Verhulst

Sara Holoubek in the Lab Report: “Prize competitions have long been used to accelerate innovation. In the 18th century, Britain offered a significant prize purse for advancements in seafaring navigation, and Napoleon’s investment in a competition led to innovation in food preservation. More recently, DARPA’s Grand Challenge ignited a decade of progress in autonomous vehicle technology.

Challenges are considered a branch of “open innovation,” an idea that has been around for decades but became more popular after the University of California’s Henry Chesbrough published a book on the topic in 2003. Chesbrough describes open innovation as “a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology.”…Here’s what we’ve learned…:

1. It’s a long game.

Clients get more out of open innovation when they reject a “one and done” mentality, opting instead to build an open innovation competency, socialize best practices across the broader organization, and determine the best moments to push the innovation envelope. …

2. Start with problem statement definition.

If a company isn’t in agreement on the problem to be solved, its challenge won’t be successful. …

3. Know what would constitute a “big win.”

Many of our clients are tasked with balancing near-term expectations while navigating what it will take for the organization to thrive in the long term. Rather than meeting in the middle, we ask what would constitute a “big win.” …

4. Invest in challenge design.

The market is flooded with platforms that aim to democratize challenges — and better access to tools is great. But in the absence of challenge design, a competition run on the best platform will fail. ….

5. Understand what it takes to close the gap between concept and viability.

…Solvers often tell us this “virtual accelerator” period — which includes education and exercises in empathy-building, subject matter knowledge, rapid prototyping, and business modeling — is of more value to their teams than prize money.

6. Hug the lawyers — as early as possible.

… Faced with unique constraints, we encourage clients to engage counsel early in the process. …

7. Really, really good marketing is essential.

A key selling point for challenge platforms is the size of their database. Some even monetize “communities.” …(More)”

Transitioning Towards a Knowledge Society: Qatar as a Case Study

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Book by Julia Gremm, Julia Barth, Kaja J. Fietkiewicz and Wolfgang G. Stock: “The book offers a critical evaluation of Qatar’s path from oil- and gas-based industries to a knowledge-based economy. This book gives basic information about the region and the country, including the geographic and demographic data, the culture, the politics and the economy, the health care conditions and the education system. It introduces the concepts of knowledge society and knowledge-based development and adds factual details about Qatar by interpreting indicators of the development status. Subsequently, the research methods that underlie the study are described, which offers information on the eGovernment study analyzing the government-citizen relationship, higher education institutions and systems, its students and the students’ way into the labor market. This book has an audience with economists, sociologists, political scientists, geographers, information scientists and other researchers on the knowledge society, but also all researchers and practitioners interested in the Arab Oil States and their future….(More)”.

How Software is Eating the World and Reprogramming Democracy

Curated on Posted on by Stefaan Verhulst

Jaime Gómez Ramírez at Open Mind: “Democracy, the government of the majority typically through elected representatives, is undergoing a major crisis. Human societies have experimented with democracy since at least the fifth century BC in the polis of Athens. Whether democracy is scalable is an open question that could help understand the current mistrust in democratic institutions and the rise of populism. The majority rule is a powerful narrative that is fed every few years with elections. In Against elections, the cultural historian Van Reybrouck claims that elections were never meant to make democracy possible, rather the opposite, it was a tool designed for those in power to prevent “the rule of the mob”. Elections created a new elite and power remained in the hands of a minority, but this time endowed with democratic legitimacy….

The 2008 financial crisis have changed the perception of, the once taken for granted, complementary nature of democracy and capitalism. The belief that capitalism and democracy go hand by hand is not credible anymore. The concept of nation is a fiction in need of a continuous stock of intergenerational believers. The nation state successfully assimilated heterogeneous groups of people under a common language and shared cultural values. But this seems today a rather fragile foundation to resist the centrifugal forces that financial capitalism impinges upon the social fabric.

Nation states will not collapse over night, but they are an industrial era device in a digital world. To do not fall into obsolescence they will need to change their operative system. Since the venture capitalist Marc Andreessen coined the phrase “software is eating the world” the logic of financial capitalism has accelerated this trend. Five software companies: Facebook, Apple, Amazon, Netflix and Google parent Alphabet (FANG) equal more than 10 per cent percent of the S&P 500 cap. Todays dominant industries in entertainment, retail, telecom, marketing companies and others are software companies. Software is also taking a bigger share in industries that traditionally exist in the physical space like automakers and energy. Education and health care have shown more resistance to software-based entrepreneurial change but a very profound transformation is underway. This is already visible with the growing popularity of MOOCs and personalized health monitoring systems.

Software-based business not only have up trending market share but more importantly, software can reprogram the world. The internet of things will allow to have full connectivity of smart devices in an economy with massive deflationary costs in computing. Computing might even become free. This has profound consequences for business, industry and most importantly, for how citizens want to organize society and governance.

The most promising technological innovation in years is the blockchain technology, an encrypted and distributed ledger system. Blockchain is an universal and freely accessible repository of documents including property and insurance contracts, publicly auditable, and resistant to special group interests manipulation and corruption. New kinds of governance models and services could be tested and implemented using the blockchain. The time is ripe for fundamental software-based transformation in governance. Democracy and free society will ignore this at its own peril…(More)”.

Scientists can now figure out detailed, accurate neighborhood demographics using Google Street View photos

Curated on Posted on by Stefaan Verhulst

Christopher Ingraham at the Washington Post: “A team of computer scientists has derived accurate, neighborhood-level estimates of the racial, economic and political characteristics of 200 U.S. cities using an unlikely data source — Google Street View images of people’s cars.

Published this week in the Proceedings of the National Academy of Sciences, the report details how the scientists extracted 50 million photographs of street scenes captured by Google’s Street View cars in 2013 and 2014. They then trained a computer algorithm to identify the make, model and year of 22 million automobiles appearing in neighborhoods in those images, parked outside homes or driving down the street.

The vehicles seen in Street View images are often small or blurry, making precise identification a challenge. So the researchers had human experts identify a small subsample of the vehicles and compare those to the results churned out by their algorithm. They that the algorithm correctly identified whether a vehicle was U.S.- or foreign-made roughly 88 percent of the time, got the manufacturer right 66 percent of the time and nailed the exact model 52 percent of the time.

While far from perfect, the sheer size of the vehicle database means those numbers are still useful for real-world statistical applications, like drawing connections between vehicle preferences and demographic data. The 22 million vehicles in the database comprise roughly 8 percent of all vehicles in the United States. By comparison, the U.S. Census Bureau’s massive American Community Survey reaches only about 1.6 percent of American householdseach year, while the typical 1,000-person opinion poll includes just 0.0004 of American adults.

To test what this data set could be capable of, the researchers first paired the Zip code-level vehicle data with numbers on race, income and education from the American Community Survey. They did this for a random 15 percent of the Zip codes in their data set to create a “training set.” They then created another algorithm to go through the training set to see how vehicle characteristics correlated with neighborhood characteristics: What kinds of vehicles are disproportionately likely to appear in white neighborhoods, or black ones? Low-income vs. high-income? Highly-educated areas vs. less-educated ones?

That yielded a number of reliable correlations….(More)”.

Leveraging the disruptive power of artificial intelligence for fairer opportunities

Curated on Posted on by Stefaan Verhulst

Makada Henry-Nickie at Brookings: “According to President Obama’s Council of Economic Advisers (CEA), approximately 3.1 million jobs will be rendered obsolete or permanently altered as a consequence of artificial intelligence technologies. Artificial intelligence (AI) will, for the foreseeable future, have a significant disruptive impact on jobs. That said, this disruption can create new opportunities if policymakers choose to harness them—including some with the potential to help address long-standing social inequities. Investing in quality training programs that deliver premium skills, such as computational analysis and cognitive thinking, provides a real opportunity to leverage AI’s disruptive power.

AI’s disruption presents a clear challenge: competition to traditional skilled workers arising from the cross-relevance of data scientists and code engineers, who can adapt quickly to new contexts. Data analytics has become an indispensable feature of successful companies across all industries. ….

Investing in high-quality education and training programs is one way that policymakers proactively attempt to address the workforce challenges presented by artificial intelligence. It is essential that we make affirmative, inclusive choices to ensure that marginalized communities participate equitably in these opportunities.

Policymakers should prioritize understanding the demographics of those most likely to lose jobs in the short-run. As opposed to obsessively assembling case studies, we need to proactively identify policy entrepreneurs who can conceive of training policies that equip workers with technical skills of “long-game” relevance. As IBM points out, “[d]ata democratization impacts every career path, so academia must strive to make data literacy an option, if not a requirement, for every student in any field of study.”

Machines are an equal opportunity displacer, blind to color and socioeconomic status. Effective policy responses require collaborative data collection and coordination among key stakeholders—policymakers, employers, and educational institutions—to  identify at-risk worker groups and to inform workforce development strategies. Machine substitution is purely an efficiency game in which workers overwhelmingly lose. Nevertheless, we can blunt these effects by identifying critical leverage points….

Policymakers can choose to harness AI’s disruptive power to address workforce challenges and redesign fair access to opportunity simultaneously. We should train our collective energies on identifying practical policies that update our current agrarian-based education model, which unfairly disadvantages children from economically segregated neighborhoods…(More)”

Participatory Grant Making: Has its Time Come?

Curated on Posted on by Stefaan Verhulst

Paper by Cynthia Gibson for the Ford Foundation: “…During the past decade, all sectors of society have faced heightened demand for greater accountability and transparency. People have become more distrustful of established institutions, they are demanding more information about issues and decisions afecting them and their families and communities, and they want more voice in decision-making processes. Technological innovation also has created new possibilities — and new pressures — for organizations and institutions to become more democratic by involving the public in their work.

Philanthropy is not immune from these trends. While for decades, philanthropy was seen as endowed foundations set up by the rich, recent years have seen a surge in crowdfunding, giving circles, donor-advised funds, and a panoply of digital giving platforms that allow anyone to be a philanthropist. Alongside these, traditions of giving from within communities that existed long before philanthropy became professionalized have become more prominent.

Philanthropy and other felds also are being reshaped by the attitudes and capacities of a new generation of young people who have grown up with the Internet and embrace its culture of transparency and bottom-up action. Additionally, there is a growing awareness that many public challenges are exceedingly complex and won’t respond to one-shot solutions from experts or institutions working on their own.

These and other trends refect a backlash against the “establishment” occurring in politics, higher education, the media, and other felds in which elite interests are perceived to have drowned out the concerns of ordinary people. Americans of all stripes and political persuasions have come to believe they have little say in guiding public decisions and improving the health and well-being of their communities..

This paper assesses the embrace of participatory approaches to date by philanthropy and other felds. In assessing philanthropy’s record, the paper fnds examples of individual foundations and networks of funders that are experimenting with participatory approaches. It also, however, fnds that there is a great deal of talk about participation in the feld but comparatively little commitment to integrating these practices into foundations’ strategies and activities, and especially their cultures, over the long term…(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)”.

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)”.

Globally, Broad Support for Representative and Direct Democracy

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Pew Global: “A deepening anxiety about the future of democracy around the world has spread over the past few years. Emboldened autocrats and rising populists have shaken assumptions about the future trajectory of liberal democracy, both in nations where it has yet to flourish and countries where it seemed strongly entrenched. Scholars have documented a global “democratic recession,” and some now warn that even long-established “consolidated” democracies could lose their commitment to freedom and slip toward more authoritarian politics.

A 38-nation Pew Research Center survey finds there are reasons for calm as well as concern when it comes to democracy’s future. More than half in each of the nations polled consider representative democracy a very or somewhat good way to govern their country. Yet, in all countries, pro-democracy attitudes coexist, to varying degrees, with openness to nondemocratic forms of governance, including rule by experts, a strong leader or the military.

A number of factors affect the depth of the public’s commitment to representative democracy over nondemocratic options. People in wealthier nations and in those that have more fully democratic systems tend to be more committed to representative democracy. And in many nations, people with less education, those who are on the ideological right and those who are dissatisfied with the way democracy is currently working in their country are more willing to consider nondemocratic alternatives.

At the same time, majorities in nearly all nations also embrace another form of democracy that places less emphasis on elected representatives. A global median of 66% say direct democracy – in which citizens, rather than elected officials, vote on major issues – would be a good way to govern. This idea is especially popular among Western European populists….(More)”

“Nudge units” – where they came from and what they can do

Curated on Posted on by Stefaan Verhulst

Zeina Afif at the Worldbank: “You could say that the first one began in 2009, when the US government recruited Cass Sunstein to head The Office of Information and Regulatory Affairs (OIRA) to streamline regulations. In 2010, the UK established the first Behavioural Insights Unit (BIT) on a trial basis, under the Cabinet Office. Other countries followed suit, including the US, Australia, Canada, Netherlands, and Germany. Shortly after, countries such as India, Indonesia, Peru, Singapore, and many others started exploring the application of behavioral insights to their policies and programs. International institutions such as the World Bank, UN agencies, OECD, and EU have also established behavioral insights units to support their programs. And just this month, the Sustainable Energy Authority of Ireland launched its own Behavioral Economics Unit.

The Future
As eMBeD, the behavioral science unit at the World Bank, continues to support governments across the globe in the implementation of their units, here are some common questions we often get asked.

What are the models for a Behavioral Insights Unit in Government?
As of today, over a dozen countries have integrated behavioral insights with their operations. While there is not one model to prescribe, the setup varies from centralized or decentralized to networked….

In some countries, the units were first established at the ministerial level. One example is MineduLab in Peru, which was set up with eMBeD’s help. The unit works as an innovation lab, testing rigorous and leading research in education and behavioral science to address issues such as teacher absenteeism and motivation, parents’ engagement, and student performance….

What should be the structure of the team?
Most units start with two to four full-time staff. Profiles include policy advisors, social psychologists, experimental economists, and behavioral scientists. Experience in the public sector is essential to navigate the government and build support. It is also important to have staff familiar with designing and running experiments. Other important skills include psychology, social psychology, anthropology, design thinking, and marketing. While these skills are not always readily available in the public sector, it is important to note that all behavioral insights units partnered with academics and experts in the field.

The U.S. team, originally called the Social and Behavioral Sciences Team, is staffed mostly by seconded academic faculty, researchers, and other departmental staff. MineduLab in Peru partnered with leading experts, including the Abdul Latif Jameel Poverty Action Lab (J-PAL), Fortalecimiento de la Gestión de la Educación (FORGE), Innovations for Poverty Action (IPA), and the World Bank….(More)”