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)”.

Automating Decision-making in Migration Policy: A Navigation Guide

Curated on Posted on by Stefaan Verhulst

Report by Astrid Ziebarth and Jessica Bither: “Algorithmic-driven or automated decision-making models (ADM) and programs are increasingly used by public administrations to assist human decision-making processes in public policy—including migration and refugee policy. These systems are often presented as a neutral, technological fix to make policy and systems more efficient. However, migration policymakers and stakeholders often do not understand exactly how these systems operate. As a result, the implications of adopting ADM technology are still unclear, and sometimes not considered. In fact, automated decision-making systems are never neutral, nor is their employment inevitable. To make sense of their function and decide whether or how to use them in migration policy will require consideration of the specific context in which ADM systems are being employed.

Three concrete use cases at core nodes of migration policy in which automated decision-making is already either being developed or tested are examined: visa application processes, placement matching to improve integration outcomes, and forecasting models to assist for planning and preparedness related to human mobility or displacement. All cases raise the same categories of questions: from the data employed, to the motivation behind using a given system, to the action triggered by models. The nuances of each case demonstrate why it is crucial to understand these systems within a bigger socio-technological context and provide categories and questions that can help policymakers understand the most important implications of any new system, including both technical consideration (related to accuracy, data questions, or bias) as well as contextual questions (what are we optimizing for?).

Stakeholders working in the migration and refugee policy space must make more direct links to current discussions surrounding governance, regulation of AI, and digital rights more broadly. We suggest some first points of entry toward this goal. Specifically, for next steps stakeholders should:

  1. Bridge migration policy with developments in digital rights and tech regulation
  2. Adapt emerging policy tools on ADM to migration space
  3. Create new spaces for exchange between migration policymakers, tech regulators, technologists, and civil society
  4. Include discussion on the use of ADM systems in international migration fora
  5. Increase the number of technologists or bilinguals working in migration policy
  6. Link tech and migration policy to bigger questions of foreign policy and geopolitics…(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)”.

Pandemic Privacy

Curated on Posted on by Stefaan Verhulst

A Preliminary Analysis of Collection Technologies, Data Collection Laws, and Legislative Reform during COVID-19 by Benjamin Ballard, Amanda Cutinha, and Christopher Parsons: “…a preliminary comparative analysis of how different information technologies were mobilized in response to COVID-19 to collect data, the extent to which Canadian health or privacy or emergencies laws impeded the response to COVID-19, and ultimately, the potential consequences of reforming data protection or privacy laws to enable more expansive data collection, use, or disclosure of personal information in future health emergencies. In analyzing how data has been collected in the United States, United Kingdom, and Canada, we found that while many of the data collection methods could be mapped onto a trajectory of past collection practices, the breadth and extent of data collection in tandem with how communications networks were repurposed constituted novel technological responses to a health crisis. Similarly, while the intersection of public and private interests in providing healthcare and government services is not new, the ability for private companies such as Google and Apple to forcefully shape some of the technology-enabled pandemic responses speaks to the significant ability of private companies to guide or direct public health measures that rely on contemporary smartphone technologies. While we found that the uses of technologies were linked to historical efforts to combat the spread of disease, the nature and extent of private surveillance to enable public action was arguably unprecedented….(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)”

Open science, data sharing and solidarity: who benefits?

Curated on Posted on by Stefaan Verhulst

Report by Ciara Staunton et al: “Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL)….(More)”.

Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework

Curated on Posted on by Stefaan Verhulst

Paper by Pranav Gupta and Anita Williams Woolley: “Human society faces increasingly complex problems that require coordinated collective action. Artificial intelligence (AI) holds the potential to bring together the knowledge and associated action needed to find solutions at scale. In order to unleash the potential of human and AI systems, we need to understand the core functions of collective intelligence. To this end, we describe a socio-cognitive architecture that conceptualizes how boundedly rational individuals coordinate their cognitive resources and diverse goals to accomplish joint action. Our transactive systems framework articulates the inter-member processes underlying the emergence of collective memory, attention, and reasoning, which are fundamental to intelligence in any system. Much like the cognitive architectures that have guided the development of artificial intelligence, our transactive systems framework holds the potential to be formalized in computational terms to deepen our understanding of collective intelligence and pinpoint roles that AI can play in enhancing it….(More)”

Data protection in the context of covid-19. A short (hi)story of tracing applications

Curated on Posted on by Stefaan Verhulst

Book edited by Elise Poillot, Gabriele Lenzini, Giorgio Resta, and Vincenzo Zeno-Zencovich: “The volume presents the results of a research project  (named “Legafight”) funded by the Luxembourg Fond National de la Recherche in order to verify if and how digital tracing applications could be implemented in the Grand-Duchy in order to counter and abate the Covid-19 pandemic. This inevitably brought to a deep comparative overview of the various existing various models, starting from that of the European Union and those put into practice by Belgium, France, Germany, and Italy, with attention also to some Anglo-Saxon approaches (the UK and Australia). Not surprisingly the main issue which had to be tackled was that of the protection of the personal data collected through the tracing applications, their use by public health authorities and the trust laid in tracing procedures by citizens. Over the last 18 months tracing apps have registered a rise, a fall, and a sudden rebirth as mediums devoted not so much to collect data, but rather to distribute real time information which should allow informed decisions and be used as repositories of health certifications…(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)”.