Decolonizing Innovation

Curated on Posted on by Stefaan Verhulst

Essay by Tony Roberts and Andrea Jimenez Cisneros: “In order to decolonize global innovation thinking and practice, we look instead to indigenous worldviews such as Ubuntu in Southern Africa, Swaraj in South Asia, and Buen Vivir in South America. Together they demonstrate that a radically different kind of innovation is possible.

The fate of Kenya’s Silicon Savannah should serve as a cautionary tale about exporting Western models to the Global South.

The fate of Kenya’s Silicon Savannah should serve as a cautionary tale about exporting Western models to the Global South. The idea of an African Silicon Valley emerged around 2011 amidst the digital technology ecosystem developing in Nairobi. The success of Nairobi’s first innovation hub inspired many imitators and drove ambitious plans by the government to build a new innovation district in the city. The term “Silicon Savannah” captured these aspirations and featured in a series of blog posts, white papers, and consultancy reports. Advocates argued that Nairobi could leapfrog other innovation centers due to lower entry barriers and cost advantages.

These promises caught the attention of many tech entrepreneurs and policymakers—including President Barack Obama, who cohosted the 2015 Global Entrepreneurship Summit in Kenya. As part of its Silicon Savannah vision, the Kenyan government proposed to build a “smart city” called Konza Technopolis in the south of Nairobi. This government-led initiative—designed with McKinsey consultants—was supposed to help turn Kenya into a “middle-income country providing a high quality life to all its citizens by the year 2030.” The city was proposed to attract investors, create jobs at a mass scale, and use technology to manage the city effectively and efficiently. Its website identified Konza as the place where “Africa’s silicon savannah begins.” Years later, the dream remains unfulfilled. As Kenyan writer Carey Baraka’s has recently detailed, the plan has only reinforced existing inequalities as it caters mainly to international multinationals and the country’s wealthy elite.

One of the most important lessons to be derived from studying such efforts to import foreign technologies and innovation models is that they inevitably come with ideological baggage. Silicon Valley is not just a theoretical model for economic growth: it represents a whole way of life, carrying with it all kinds of implications for how people think about themselves, each other, and their place in the world. Venture capital pitching sessions prize what is most monetizable, what stands to deliver the greatest return on investment, and what offers the earliest exit opportunities. Breznitz is right to criticize this way of thinking, but similar worries arise about his own examples, which say little about environmental sustainability or maintaining the integrity of local communities. Neoliberal modes of private capital accumulation are not value neutral, and we must be sensitive to the way innovation models are situated in uneven structures of power, discourse, and resource distribution…(More)”.

Randomistas vs. Contestistas

Curated on Posted on by Stefaan Verhulst

Excerpt by By Beth Simone Noveck: “Social scientists who either run experiments or conduct systematic reviews tend to be fervent proponents of the value of RCTs. But that evidentiary hierarchy—what some people call the “RCT industrial complex”—may actually lead us to discount workable solutions just because there is no accompanying RCT.

A trawl of the solution space shows that successful interventions developed by entrepreneurs in business, philanthropy, civil society, social enterprise, or business schools who promote and study open innovation, often by developing and designing competitions to source ideas, often come from more varied places. Uncovering these exciting social innovations lays bare the limitations of confining a definition of what works only to RCTs.

Many more entrepreneurial and innovative solutions are simply not tested with an RCT and are not the subject of academic study. As one public official said to me, you cannot saddle an entrepreneur with having to do a randomized controlled trial (RCT), which they do not have the time or know-how to do. They are busy helping real people, and we have to allow them “to get on with it.”

For example, MIT Solve, which describes itself as a marketplace for socially impactful innovation designed to identify lasting solutions to the world’s most pressing problems. It catalogs hundreds of innovations in use around the world, like Faircap, a chemical-free water filter used in Mozambique, or WheeLog!, an application that enables individuals and local governments to share accessibility information in Tokyo.

Research funding is also too limited (and too slow) for RCTs to assess every innovation in every domain. Many effective innovators do not have the time, resources, or know-how to partner with academic researchers to conduct a study, or they evaluate projects by some other means.

There are also significant limitations to RCTs. For a start, systematic evidence reviews are quite slow, frequently taking upward of two years, and despite published standards for review, there is a lack of transparency. Faster approaches are important. In addition, many solutions that have been tested with an RCT clearly do not work. Interestingly, the first RCT in an area tends to produce an inflated effect size….(More)”.

Collective innovation is key to the lasting successes of democracies

Curated on Posted on by Stefaan Verhulst

Article by Kent Walker and Jared Cohen: “Democracies across the world have been through turbulent times in recent years, as polarization and gridlock have posed significant challenges to progress. The initial spread of COVID-19 spurred chaos at the global level, and governments scrambled to respond. With uncertainty and skepticism at an all-time high, few of us would have guessed a year ago that 66 percent of Americans would have received at least one vaccine dose by now. So what made that possible?

It turns out democracies, unlike their geopolitical competitors, have a secret weapon: collective innovation. The concept of collective innovation draws on democratic values of openness and pluralism. Free expression and free association allow for cooperation and scientific inquiry. Freedom to fail leaves room for risk-taking, while institutional checks and balances protect from state overreach.

Vaccine development and distribution offers a powerful case study. Within days of the coronavirus being first sequenced by Chinese researchers, research centers across the world had exchanged viral genome data through international data-sharing initiatives. The Organization for Economic Cooperation and Development found that 75 percent of COVID-19 research published after the outbreak relied on open data. In the United States and Europe, in universities and companies, scientists drew on open information, shared research, and debated alternative approaches to develop powerful vaccines in record-setting time.

Democracies’ self- and co-regulatory frameworks have played a critical role in advancing scientific and technological progress, leading to robust capital markets, talent-attracting immigration policies, world-class research institutions, and dynamic manufacturing sectors. The resulting world-leading productivity underpins democracies’ geopolitical influence….(More)”.

Manufacturing Consensus

Curated on Posted on by Stefaan Verhulst

Essay by M. Anthony Mills: “…Yet, the achievement of consensus within science, however rare and special, rarely translates into consensus in social and political contexts. Take nuclear physics, a well-established field of natural science if ever there were one, in which there is a high degree of consensus. But agreement on the physics of nuclear fission is not sufficient for answering such complex social, political, and economic questions as whether nuclear energy is a safe and viable alternative energy source, whether and where to build nuclear power plants, or how to dispose of nuclear waste. Expertise in nuclear physics and literacy in its consensus views is obviously important for answering such questions, but inadequate. That’s because answering them also requires drawing on various other kinds of technical expertise — from statistics to risk assessment to engineering to environmental science — within which there may or may not be disciplinary consensus, not to mention grappling with practical challenges and deep value disagreements and conflicting interests.

It is in these contexts — where multiple kinds of scientific expertise are necessary but not sufficient for solving controversial political problems — that the dependence of non-experts on scientific expertise becomes fraught, as our debates over pandemic policies amply demonstrate. Here scientific experts may disagree about the meaning, implications, or limits of what they know. As a result, their authority to say what they know becomes precarious, and the public may challenge or even reject it. To make matters worse, we usually do not have the luxury of a scientific consensus in such controversial contexts anyway, because political decisions often have to be made long before a scientific consensus can be reached — or because the sciences involved are those in which a consensus is simply not available, and may never be.

To be sure, scientific experts can and do weigh in on controversial political decisions. For instance, scientific institutions, such as the National Academies of Sciences, will sometimes issue “consensus reports” or similar documents on topics of social and political significance, such as risk assessment, climate change, and pandemic policies. These usually draw on existing bodies of knowledge from widely varied disciplines and take considerable time and effort to produce. Such documents can be quite helpful and are frequently used to aid policy and regulatory decision-making, although they are not always available when needed for making a decision.

Yet the kind of consensus expressed in these documents is importantly distinct from the kind we have been discussing so far, even though they are both often labeled as such. The difference is between what philosopher of science Stephen P. Turner calls a “scientific consensus” and a “consensus of scientists.” A scientific consensus, as described earlier, is a relatively stable paradigm that structures and organizes scientific research. By contrast, a consensus of scientists is an organized, professional opinion, created in response to an explicit political or social need, often an official government request…(More)”.

If We Can Report on the Problem, We Can Report on the Solution

Curated on Posted on by Stefaan Verhulst

David Bornstein and Tina Rosenberg in the New York Times: “After 11 years and roughly 600 columns, this is our last….

David Bornstein: Tina, in a decade reporting on solutions, what’s the most important thing you learned?

Tina Rosenberg: This is a strange lesson for a column about new ideas and innovation, but I learned that they’re overrated. The world (mostly) doesn’t need new inventions. It needs better distribution of what’s already out there.

Some of my favorite columns were about how to take old ideas or existing products and get them to new people. As one of our columns put it, “Ideas Help No One on a Shelf. Take Them to the World.” There are proven health strategies, for example, that never went anywhere until some folks dusted them off and decided to spread them. It’s not glamorous to copy another idea. But those copycats are making a big difference.

David: I totally agree. The opportunity to learn from other places is hugely undertapped.

I mean, in the United States alone, there are over 3,000 counties. The chance that any one of them is struggling with big problems — mental health, addiction, climate change, diabetes, Covid-19, you name it — is pretty much 100 percent. But the odds that any place is actually using one of the most effective approaches to deal with its problems is quite low.

As you know, I used to be a computer programmer, and I’m still a stats nerd. With so many issues, there are “positive deviants” — say, 2 percent or 3 percent of actors who are getting significantly better results than the norm. Finding those outliers, figuring out what they’re doing that’s different, and sharing the knowledge can really help. I saw this in my reporting on childhood trauma, chronic homelessness and hospital safety, to name a few areas….(More)”

Are we really so polarised?

Curated on Posted on by Stefaan Verhulst

Article by Dominic Packer and Jay Van Bavel: “In 2020, the match-making website OkCupid asked 5 million hopeful daters around the world: “Could you date someone who has strong political opinions that are the opposite of yours?” Sixty per cent said no, up from 53% a year before.

Scholars used to worry that societies might not be polarised enough. Without clear differences between political parties, they thought, citizens lack choices, and important issues don’t get deeply debated. Now this notion seems rather quaint as countries have fractured along political lines, reflected in everything from dating preferences to where people choose to live.

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Just how stark has political polarisation become? Well, it depends on where you live and how you look at it. When social psychologists study relations between groups, they often find that whereas people like their own groups a great deal, they have fairly neutral feelings towards out-groups: “They’re fine, but we’re great!” This pattern used to describe relations between Democrats and Republicans in the US. In 1980, partisans reported feeling warm towards members of their own party and neutral towards people on the other side. However, while levels of in-party warmth have remained stable since then, feelings towards the out-party have plummeted.

The dynamics are similar in the UK, where the Brexit vote was deeply divisive. A 2019 study revealed that while UK citizens were not particularly identified with political parties, they held strong identities as remainers or leavers. Their perceptions were sharply partisan, with each side regarding its supporters as intelligent and honest, while viewing the other as selfish and close-minded. The consequences of hating political out-groups are many and varied. It can lead people to support corrupt politicians, because losing to the other side seems unbearable. It can make compromise impossible even when you have common political ground. In a pandemic, it can even lead people to disregard advice from health experts if they are embraced by opposing partisans.

The negativity that people feel towards political opponents is known to scientists as affective polarisation. It is emotional and identity-driven – “us” versus “them”. Importantly, this is distinct from another form of division known as ideological polarisation, which refers to differences in policy preferences. So do we disagree about the actual issues as much as our feelings about each other suggest?

Despite large differences in opinion between politicians and activists from different parties, there is often less polarisation among regular voters on matters of policy. When pushed for their thoughts about specific ideas or initiatives, citizens with different political affiliations often turn out to agree more than they disagree (or at least the differences are not as stark as they imagine).

More in Common, a research consortiumthat explores the drivers of social fracturing and polarisation, reports on areas of agreement between groups in societies. In the UK, for example, they have found that majorities of people across the political spectrum view hate speech as a problem, are proud of the NHS, and are concerned about climate change and inequality…(More)”.

‘Is it OK to …’: the bot that gives you an instant moral judgment

Curated on Posted on by Stefaan Verhulst

Article by Poppy Noor: “Corporal punishment, wearing fur, pineapple on pizza – moral dilemmas, are by their very nature, hard to solve. That’s why the same ethical questions are constantly resurfaced in TV, films and literature.

But what if AI could take away the brain work and answer ethical quandaries for us? Ask Delphi is a bot that’s been fed more than 1.7m examples of people’s ethical judgments on everyday questions and scenarios. If you pose an ethical quandary, it will tell you whether something is right, wrong, or indefensible.

Anyone can use Delphi. Users just put a question to the bot on its website, and see what it comes up with.

The AI is fed a vast number of scenarios – including ones from the popular Am I The Asshole sub-Reddit, where Reddit users post dilemmas from their personal lives and get an audience to judge who the asshole in the situation was.

Then, people are recruited from Mechanical Turk – a market place where researchers find paid participants for studies – to say whether they agree with the AI’s answers. Each answer is put to three arbiters, with the majority or average conclusion used to decide right from wrong. The process is selective – participants have to score well on a test to qualify to be a moral arbiter, and the researchers don’t recruit people who show signs of racism or sexism.

The arbitrators agree with the bot’s ethical judgments 92% of the time (although that could say as much about their ethics as it does the bot’s)…(More)”.

AI Generates Hypotheses Human Scientists Have Not Thought Of

Curated on Posted on by Stefaan Verhulst

Robin Blades in Scientific American: “Electric vehicles have the potential to substantially reduce carbon emissions, but car companies are running out of materials to make batteries. One crucial component, nickel, is projected to cause supply shortages as early as the end of this year. Scientists recently discovered four new materials that could potentially help—and what may be even more intriguing is how they found these materials: the researchers relied on artificial intelligence to pick out useful chemicals from a list of more than 300 options. And they are not the only humans turning to A.I. for scientific inspiration.

Creating hypotheses has long been a purely human domain. Now, though, scientists are beginning to ask machine learning to produce original insights. They are designing neural networks (a type of machine-learning setup with a structure inspired by the human brain) that suggest new hypotheses based on patterns the networks find in data instead of relying on human assumptions. Many fields may soon turn to the muse of machine learning in an attempt to speed up the scientific process and reduce human biases.

In the case of new battery materials, scientists pursuing such tasks have typically relied on database search tools, modeling and their own intuition about chemicals to pick out useful compounds. Instead a team at the University of Liverpool in England used machine learning to streamline the creative process. The researchers developed a neural network that ranked chemical combinations by how likely they were to result in a useful new material. Then the scientists used these rankings to guide their experiments in the laboratory. They identified four promising candidates for battery materials without having to test everything on their list, saving them months of trial and error…(More)”.

Countries’ climate pledges built on flawed data

Curated on Posted on by Stefaan Verhulst

Article by Chris Mooney, Juliet Eilperin, Desmond Butler, John Muyskens, Anu Narayanswamy, and Naema Ahmed: “Across the world, many countries underreporttheir greenhouse gas emissions in their reports to the United Nations, a Washington Post investigation has found. An examination of 196 country reports reveals a giant gap between what nations declare their emissions to be versus the greenhouse gases they are sending into the atmosphere. The gap ranges from at least 8.5 billion to as high as 13.3 billion tons a year of underreported emissions — big enough to move the needle on how much the Earth will warm.

The plan to save the world from the worst of climate change is built on data. But the data the world is relying on is inaccurate.

“If we don’t know the state of emissions today, we don’t know whether we’re cutting emissions meaningfully and substantially,” said Rob Jackson, a professor at Stanford University and chair of the Global Carbon Project, a collaboration of hundreds of researchers. “The atmosphere ultimately is the truth. The atmosphere is what we care about. The concentration of methane and other greenhouse gases in the atmosphere is what’s affecting climate.”

At the low end, the gap is larger than the yearly emissions of the United States. At the high end, it approaches the emissions of China and comprises 23 percent of humanity’s total contribution to the planet’s warming, The Post found…

A new generation of sophisticated satellites that can measure greenhouse gases are now orbiting Earth, and they can detect massive methane leaks. Data from the International Energy Agency (IEA) lists Russia as the world’s top oil and gas methane emitter, but that’s not what Russia reports to the United Nations. Its official numbers fall millions of tons shy of what independent scientific analyses show, a Post investigation found. Many oil and gas producers in the Persian Gulf region, such as the United Arab Emirates and Qatar, also report very small levels of oil and gas methane emission that don’t line up with other scientific data sets.

“It’s hard to imagine how policymakers are going to pursue ambitious climate actions if they’re not getting the right data from national governments on how big the problem is,” said Glenn Hurowitz, chief executive of Mighty Earth, an environmental advocacy group….(More)”.

Embrace Complexity Through Behavioral Planning

Curated on Posted on by Stefaan Verhulst

Article by Ruth Schmidt and Katelyn Stenger: “…Designing for complexity also requires questioning assumptions about how interventions work within systems. Being wary of three key assumptions about persistence, stability, and value can help behavioral designers recognize changes over time, complex system dynamics, and oversimplified definitions of success that may impact the effectiveness of interventions.

When behavioral designers overlook these assumptions, the solutions they recommend risk being short-sighted, nonstrategic, and destined to be reactive rather than proactive. Systematically confronting and planning for these projections, on the other hand, can help behavioral designers create and situate more resilient interventions within complex systems.

In a recent article, we explored why behavioral science is still learning to grapple with complexity, what it loses when it doesn’t, and what it could gain by doing so in a more strategic and systematic way. This approach—which we call “behavioral planning”—borrows from business strategy practices like scenario planning that play out assumptions about plausible future conditions to test how they might impact the business environment. The results are then used to inform “roughly right” directional decisions about how to move forward…(More)”