Wikipedia @ 20

Stories of an Incomplete Revolution edited by Joseph Reagle and Jackie Koerner (Open Access): “We have been looking things up in Wikipedia for twenty years. What began almost by accident—a wiki attached to a nascent online encyclopedia—has become the world’s most popular reference work. Regarded at first as the scholarly equivalent of a Big Mac, Wikipedia is now known for its reliable sourcing and as a bastion of (mostly) reasoned interaction. How has Wikipedia, built on a model of radical collaboration, remained true to its original mission of “free access to the sum of all human knowledge” when other tech phenomena have devolved into advertising platforms? In this book, scholars, activists, and volunteers reflect on Wikipedia’s first twenty years, revealing connections across disciplines and borders, languages and data, the professional and personal.

The contributors consider Wikipedia’s history, the richness of the connections that underpin it, and its founding vision. Their essays look at, among other things, the shift from bewilderment to respect in press coverage of Wikipedia; Wikipedia as “the most important laboratory for social scientific and computing research in history”; and the acknowledgment that “free access” includes not just access to the material but freedom to contribute—that the summation of all human knowledge is biased by who documents it….(More)”

Trace Labs

Trace Labs is a nonprofit organization whose mission is to accelerate
the family reunification of missing persons while training members in
the trade craft of open source intelligence (OSINT)….We crowdsource open source intelligence through both the Trace Labs OSINT Search Party CTFs and Ongoing Operations with our global community. Our highly skilled intelligence analysts then triage the data collected to produce actionable intelligence reports on each missing persons subject. These intelligence reports allow the law enforcement agencies that we work with the ability to quickly see any new details required to reopen a cold case and/or take immediate action on a missing subject.(More)”

Covid-19 is reshaping collective intelligence

Chris Zollinger at Diplomatic Courier: “What a difference a year makes. A survey in April showed that almost 40% of people in the EU had switched to remote work, while estimates in the U.S. range from 30-50%. The video conference has become a staple of our daily working lives in a way that would have been inconceivable 12 months ago, while virtual collaboration tools have become ubiquitous.  

Given the straightened economic climate, it is unsurprising that many businesses see the situation as an opportunity to permanently reduce their cost base. Facebook, for example, has announced that it expects half of its global workforce to work remotely within the next five to ten years, with Twitter, Barclays and Mondelez International making similar moves. On a purely financial level, this seems like a win-win for everyone concerned: employers can save on the capital and operational costs of providing office space, while employees can save the time and money that it would have cost to commute.

However, if we want to move beyond mere economic survival towards recovery and growth, we need to be more ambitious in our thinking. Rather than merely cutting costs, we now have the chance to drive greater innovation and productivity by building more flexible, remote teams. In addition to the cost and time savings associated with remote work, companies now have an opportunity to shift the focus of their recruitment to new geographic areas and hire talented new employees without the need for them to physically relocate. In this way, they can form purpose-built teams to solve specific tasks over a defined time period….(More)”.

Using Collective Intelligence to Solve Public Problems

Report by The GovLab and the Centre for Collective Intelligence Design at Nesta: “…The experience, expertise and passion of a group of people is what we call collective intelligence. The practice of taking advantage of collective intelligence is sometimes called crowdsourcing, collaboration, co-creation or just engagement. But whatever the name, we shall explore the advantages created when institutions mobilise the information, knowledge, skills and capabilities of a distributed group to extend our problemsolving ability. Smartphone apps like PulsePoint in the United States and GoodSAM in the United Kingdom, for example, enable a network of volunteer first responders to augment the capacity of formal first responders and give
cardiopulmonary resuscitation (CPR) to a heart attack victim in the crucial, potentially lifesaving minutes before ambulance services can arrive. Deliberative ‘mini-publics’, where a small group of citizens work face to face or online to weigh up the pros and cons of alternative policy choices, have helped governments in Ireland and Australia achieve consensus on issues that previously divided both the public and politicians. In Helsinki, residents’ involvement in crafting the city’s budget and its sustainability plan is helping to strengthen the alignment between city policy and local priorities.

Despite these successes, too often leaders do not know how to engage with the public efficiently to solve problems. They may run the occasional
crowdsourcing exercise, citizens’ jury or prizebacked challenge, but they struggle to integrate collective intelligence in the regular course of business.

Citizen engagement is largely viewed as a nice-to-have rather than a must-have for efficient and effective problem-solving. Working more openly and collaboratively requires institutions to develop new capabilities, change
long-standing procedures, shift organisational cultures, foster conditions more conducive to external partnerships, alter laws and ensure collective intelligence inputs are transparently accounted for when making decisions. But knowing how to make these changes, and how to redesign the way public institutions make decisions, requires a much deeper and more nuanced understanding….(More)”.

An exploration of Augmented Collective Intelligence

Dark Matter Laboratories: “…As with all so-called wicked problems, the climate crisis occurs at the intersection of human and natural systems, where interdependent components interact at multiple scales causing uncertainty and emergent, erratic fluctuations. Interventions in such systems can trigger disproportionate impacts in other areas due to feedback effects. On top of this, collective action problems, such as identifying and implementing climate crisis adaptation or mitigation strategies, involve trade-offs and conflicting motivations between the different decision-makers. All of this presents challenges when identifying solutions, or even agreeing on a shared definition of the problem.

As is often the case in times of crisis, collective community-led actions have been a vital part of the response to the COVID-19 pandemic. Communities have demonstrated their capacity to mobilise efficiently in areas where the public sector has been either too slow, unable, or unwilling to intervene. Yet, the pandemic has also put into perspective the scale of response required to address the climate crisis. Despite a near-total shutdown of the global economy, annual CO2 emissions are only expected to fall by 5.6% this year, falling short of the 7.6% target required to ensure a temperature rise of no more than 1.5°C. Can AI help amplify and coordinate collective action to the scale necessary for effective climate crisis response? In this post, we explore alternative futures that leverage the significant potential of citizen groups to act at a local level in order to achieve global impact.

Applying AI to climate problems

There are various research collaborations, open challenges, and corporate-led initiatives that already exist in the field of AI and climate crisis. Climate Change AI, for instance, has identified a range of opportunity domains for a selection of machine learning (ML) methods. These applications range from electrical systems and transportation to collective decisions and education.’s Impact Challenge supports initiatives applying AI for social good, while the AI for Good platform aims to identify practical applications of AI that can be scaled for global impact. These initiatives and many others, such as Project Drawdown, have informed our research into opportunity areas for AI to augment Collective Intelligence.

Throughout the project, we have been wary that attempts to apply AI to complex problems can suffer from technological solutionism, which loses sight of the underlying issues. To try to avoid this, with Civic AI, we have focused on understanding community challenges before identifying which parts of the problem are most suited to AI’s strengths, especially as this is just one of the many tools available. Below, we explore how AI could be used to complement and enhance community-led efforts as part of inclusive civic infrastructures.

We define civic assets as the essential shared infrastructure that benefits communities such as an urban forest or a community library. We will explore their role in climate crisis mitigation and adaptation. What does a future look like in which these assets are semi-autonomous and highly participatory, fostering collaboration between people and machines?…(More) –

See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern

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Might social intelligence save Latin America from its governments in times of Covid-19?

Essay by Thamy Pogrebinschi: “…In such scenarios, it seems relevant to acknowledge the limits of the state to deal with huge and unpredictable challenges and thus the need to resort to civil society. State capacity cannot be built overnight, but social intelligence is an unlimited and permanently available resource. In recent years, digital technology has multiplied what has been long called social intelligence (Dewey) and is now more often known as collective intelligence (Lévy), the wisdom of crowds (Surowiecki), or democratic reason (Landemore).

Taken together, these concepts point to the most powerful tool available to governments facing hard problems and unprecedented challenges: the sourcing and sharing of knowledge, information, skills, resources, and data from citizens in order to address social and political problems.

The Covid-19 pandemic presents an opportunity to test the potential of social intelligence as fuel for processes of creative collaboration that may aid governments to reinvent themselves and prepare for the challenges that will remain after the virus is gone. By creative collaboration, I mean a range of forms of communication, action, and connection among citizens themselves, between citizens and civil society organizations (CSOs), and between the latter two and their governments, all with the common aim of addressing problems that affect all and that the state for various reasons cannot (satisfactorily) respond to alone.

While several Latin American countries have been stuck in the Covid-19 crisis with governments unable or unwilling to contain it or to reduce its damages, a substantial number of digital democratic innovations have been advanced by civil society in the past few months. These comprise institutions, processes, and mechanisms that rely on digital citizen participation as a means to address social and political problems – and, more recently, also problems of a humanitarian nature….

Between March 16 and July 1 of this year, at least 400 digital democratic innovations were created across 18 countries in Latin America with the specific aim of handling the Covid-19 crisis and mitigating its impact, according to recent data from the LATINNO project. These innovations are essentially mechanisms and processes in which citizens, with the aid of digital tools, are enabled to address social, political, and humanitarian problems related to the pandemic.

Citizens engage in and contribute to three levels of responses, which are based on information, connection, and action. About one-fourth of these digital democratic innovations clearly rely on crowdsourcing social intelligence.

The great majority of those digital innovations have been developed by CSOs. Around 75% of them have no government involvement at all, which is striking in a region known for implementing state-driven citizen participation as a result of the democratization processes that took place in the late 20th century. Civil society has stepped in in most countries, particularly where government responses were absent (Brazil and Nicaragua), slow (Mexico), insufficient due to lack of economic resources (Argentina) or infrastructure (Peru), or simply inefficient (Chile).

Based on these data from 18 Latin American countries, one can observe that digital democratic innovations address challenges posed by the Covid-19 outbreak in five main ways: first, generating verified information and reliable data; second, geolocating problems, needs, and demands; third, mobilizing resources, skills, and knowledge to address those problems, needs, and demands; fourth, connecting demand (individuals and organizations in need) and supply (individuals and organizations willing to provide whatever is needed); and fifth and finally, implementing and monitoring public policies and actions. In some countries, there is a sixth use that cuts across the other five: assisting vulnerable groups such as the elderly, women, children and youth, indigenous peoples, and Afro-descendants….(More)”

Designing Governance as Collective Intelligence

Paper by Hamed Khaledi: “This research models governance as a collective intelligence process, particularly as a collective design process. The outcome of this process is a solution to a problem. The solution can be a decision, a policy, a product, a financial plan, etc. The quality (value) of the outcome solution reflects the quality (performance) of the process. Using an analytical model, I identify five mediators (channels) through which, different factors and features can affect the quality of the outcome and thus the process. Based on this model, I propose an asymmetric response surface method that introduces factors to the experimental model considering their plausible effects.

As a proof of concept, I implemented a generic collective design process in a web application and measured the effects of several factors on its performance through online experiments. The results demonstrate the effectiveness of the proposed method. They also show that approval voting is significantly superior to plurality voting. Some studies assert that not the design process, but the designers drive the quality of the outcome. However, this study shows that the characteristics of the design process (e.g. voting schemes) as well as the designers (e.g. expertise and gender) can significantly affect the quality of the outcome. Hence, the outcome quality can be used as an indicator of the performance of the process. This enables us to evaluate and compare governance mechanisms objectively free from fairness criteria….(More)”.

German coronavirus experiment enlists help of concertgoers

Philip Oltermann at the Guardian: “German scientists are planning to equip 4,000 pop music fans with tracking gadgets and bottles of fluorescent disinfectant to get a clearer picture of how Covid-19 could be prevented from spreading at large indoor concerts.

As cultural mass gatherings across the world remain on hold for the foreseeable future, researchers in eastern Germany are recruiting volunteers for a “coronavirus experiment” with the singer-songwriter Tim Bendzko, to be held at an indoor stadium in the city of Leipzig on 22 August.

Participants, aged between 18 and 50, will wear matchstick-sized “contact tracer” devices on chains around their necks that transmit a signal at five-second intervals and collect data on each person’s movements and proximity to other members of the audience.

Inside the venue, they will also be asked to disinfect their hands with a fluorescent hand-sanitiser – designed to not just add a layer of protection but allow scientists to scour the venue with UV lights after the concerts to identify surfaces where a transmission of the virus through smear infection is most likely to take place.

Vapours from a fog machine will help visualise the possible spread of coronavirus via aerosols, which the scientists will try to predict via computer-generated models in advance of the event.

The €990,000 cost of the Restart-19 project will be shouldered between the federal states of Saxony and Saxony-Anhalt. The project’s organisers say the aim is to “identify a framework” for how larger cultural and sports events could be held “without posing a danger for the population” after 30 September….

To stop the Leipzig experiment from becoming the source of a new outbreak, signed-up volunteers will be sent a DIY test kit and have a swab at a doctor’s practice or laboratory 48 hours before the concert starts. Those who cannot show proof of a negative test at the door will be denied entry….(More)”.

Coronavirus: how the pandemic has exposed AI’s limitations

Kathy Peach at The Conversation: “It should have been artificial intelligence’s moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact – hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management – the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

The unlikely hero

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations – something that highly-specialised AI systems just can’t do. At least yet….

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to “follow the herd” by failing to hear diverse or minority views, or challenge assumptions – all of which are criticisms that have been levelled at the British government’s scientific advisory committees throughout the pandemic…(More)”.

Harnessing the collective intelligence of stakeholders for conservation

Paper by Steven Gray et al: ” Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI ), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans.

Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations….(More)”.