Online collective intelligence course aims to improve responses to COVID-19 and other crises

Curated on Posted on by Stefaan Verhulst

PressRelease: “Working with 11 partner institutions around the world,  The Governance Lab (The GovLab) at the New York University Tandon School of Engineering today launches a massive open online course (MOOC) on “Collective Crisis Intelligence.” The course is free, open to anyone, and designed to help institutions improve disaster response through the use of data and volunteer participation. 

Thirteen modules have been created by leading global experts in major disasters such as the post-election violence in Kenya in 2008, the Fukushima nuclear plant disaster in 2011, the Ebola crisis in 2014, the Zika outbreak in 2016, and the current coronavirus. The course is designed to help those responding to coronavirus make use of volunteerism. 

As the COVID-19 pandemic reaches unprecedented proportions and spreads to more than 150 countries on six continents, policymakers are struggling to answer questions such as “How do we predict how the virus will spread?,” “How do we help the elderly and the homebound?,” “How do we provide economic assistance to those affected by business closures?,” and more. 

In each mini-lecture, those who have learned how to mobilize groups of people online to manage in a crisis present the basic concepts and tools to learn, analyze, and implement a crowdsourced public response. Lectures include

  • Introduction: Why Collective Intelligence Matters in a Crisis
  • Defining Actionable Problems (led by Matt Andrews, Harvard Kennedy School)
  • Three Day Evidence Review (led by Peter Bragge, Monash University, Australia)
  • Priorities for Collective Intelligence (led by Geoff Mulgan, University College London) 
  • Smarter Crowdsourcing (led by Beth Simone Noveck, The GovLab)
  • Crowdfunding (led by Peter Baeck, Nesta, United Kingdom)
  • Secondary Fall Out (led by Azby Brown, Safecast, Japan)
  • Crowdsourcing Surveillance (led by Tolbert Nyenswah, Johns Hopkins Bloomberg School of Public Health, United States/Liberia)
  • Crowdsourcing Data (led by Angela Oduor Lungati and Juliana Rotich, Ushahidi, Kenya)
  • Mobilizing a Network (led by Sean Bonner, Safecast, Japan)
  • Crowdsourcing Scientific Expertise (led by Ali Nouri, Federation of American Scientists)
  • Chatbots and Social Media Strategies for Crisis (led by Nashin Mahtani, PetaBencana.id, Indonesia)
  • Conclusion: Lessons Learned

The course explores such innovative uses of crowdsourcing as Safecast’s implementation of citizen science to gather information about environmental conditions after the meltdown of the Fukushima nuclear plant; Ushahidi, an online platform in Kenya for crowdsourcing data for crisis relief, human rights advocacy, transparency, and accountability campaigns; and “Ask a Scientist,” an interactive tool developed by The GovLab with the Federation of American Scientists and the New Jersey Office of Innovation, in which a network of scientists answer citizens’ questions about COVID-19.

More information on the courses is available at https://covidcourse.thegovlab.org “

Collective Intelligence at EU Level – Social and Democratic Dimensions

Curated on Posted on by Stefaan Verhulst

Paper by Nora Milotay and Gianluca Sgueo: “Humans are among the many living species capable of collaborative and imaginative thinking. While it is widely agreed among scholars that this capacity has contributed to making humans the dominant species, other crucial questions remain open to debate. Is it possible to encourage large groups of people to engage in collective thinking? Is it possible to coordinate citizens to find solutions to address global challenges? Some scholars claim that large groups of independent, motivated, and well-informed people can, collectively, make better decisions than isolated individuals can – what is known as ‘collective intelligence.’

The social dimension of collective intelligence mainly relates to social aspects of the economy and of innovation. It shows that a holistic approach to innovation – one that includes not only technological but also social aspects – can greatly contribute to the EU’s goal of promoting a just transition for everyone to a sustainable and green economy in the digital age. The EU has been taking concrete action to promote social innovation by supporting the development of its theory and practice. Mainly through funding programmes, it helps to seek new types of partners and build new capacity – and thus shape the future of local and national innovations aimed at societal needs.

The democratic dimension suggests that the power of the collective can be leveraged so as to improve public decision-making systems. Supported by technology, policy-makers can harness the ‘civic surplus’ of citizens – thus providing smarter solutions to regulatory challenges. This is particularly relevant at EU level in view of the planned Conference on the Future of Europe, aimed at engaging communities at large and making EU decision-making more inclusive and participatory.

The current coronavirus crisis is likely to change society and our economy in ways as yet too early to predict, but recovery after the crisis will require new ways of thinking and acting to overcome common challenges, and thus making use of our collective intelligence should be more urgent than ever. In the longer term, in order to mobilise collective intelligence across the EU and to fully exploit its innovative potential, the EU needs to strengthen its education policies and promote a shared understanding of a holistic approach to innovation and of collective intelligence – and thus become a ‘global brain,’ with a solid institutional set-up at the centre of a subsidised experimentation process that meets the challenges imposed by modernday transformations…(More)”.

Doctors Turn to Social Media to Develop Covid-19 Treatments in Real Time

Curated on Posted on by Stefaan Verhulst

Michael Smith and Michelle Fay Cortez at Bloomberg: “There is a classic process for treating respiratory problems: First, give the patient an oxygen mask, or slide a small tube into the nose to provide an extra jolt of oxygen. If that’s not enough, use a “Bi-Pap” machine, which pushes air into the lungs more forcefully. If that fails, move to a ventilator, which takes over the patient’s breathing.

But these procedures tend to fail With Covid-19 patients. Physicians found that by the time they reached that last step, it was often too late; the patient was already dying.

In past pandemics like the 2003 global SARS outbreak, doctors sought answers to such mysteries from colleagues in hospital lounges or maybe penned articles for medical journals. It could take weeks or months for news of a breakthrough to reach the broader community.

For Covid-19, a kind of medical hive mind is on the case. By the tens of thousands, doctors are joining specialized social media groups to develop answers in real time. One of them, a Facebook group called the PMG COVID19 Subgroup, has 30,000 members worldwide….

Doctors are trying to fill an information void online. Sabry, an emergency-room doctor in two hospitals outside Los Angeles, found that the 70,000-strong, Physician Moms Group she started five years ago on Facebook was so overwhelmed by coronavirus threads that she created the Covid-19 offshoot. So many doctors tried to join the new subgroup that Facebook’s click-to-join code broke. Some 10,000 doctors waited in line as the social media company’s engineers devised a fix.

She’s not alone. The topic also consumed two Facebook groups started by Dr. Nisha Mehta, a 38-year-old radiologist from Charlotte, North Carolina. The 54,000-member Physician Side Gigs, intended for business discussions, and an 11,000-person group called Physician Community for more general topics, are also all coronavirus, all the time, with thousands waiting to join…(More)”.

Ask a Scientist

Curated on Posted on by Stefaan Verhulst

NYU Press Release: “Unreliable tips on how to protect oneself from the novel coronavirus and fake news about the COVID-19 pandemic are spreading as quickly as the virus itself.

The Governance Lab (The GovLab) at the New York University Tandon School of Engineering has collaborated with the Federation of American Scientists (FAS) and the State of New Jersey Office of Innovation to launch a free, interactive tool aimed at cutting through the noise and presenting clear, scientist-led, and evidence-based information and advice to the public.

Available in English and Spanish, “Ask a Scientist,” allows users to find answers to a wide range of commonly asked questions about the virus, the severity of the outbreak, best methods of prevention, and steps to take in the event you fall ill. All posted content is obtained from the World Health Organization, the Centers for Disease Control and Prevention, and other rigorously verified sources.

screenshot of website that allows users to type in questions about COVID-19

“Ask a Scientist” features a free, interactive tool allowing users to submit questions to a team of FAS researchers and a crowdsourced network of vetted science experts. In English and Spanish, the site also includes top articles and the latest information, and answers to a wide range of commonly asked questions about the COVID-19 epidemic, the severity of the outbreak, best methods of prevention, and steps to take in the event you fall ill.

If users do not find an answer to their specific questions, they have the option of submitting them to a team of FAS researchers and a crowdsourced network of vetted science experts led by the National Science Policy Network. Users can expect an answer within an hour, although that timeframe is expected to shorten as the network increases in size. Every answer is reviewed to ensure accuracy and timeliness, then added to the knowledge base for the benefit of others….(More)”.

Coronavirus: seven ways collective intelligence is tackling the pandemic

Curated on Posted on by Stefaan Verhulst

Article by Kathy Peach: “Tackling the emergence of a new global pandemic is a complex task. But collective intelligence is now being used around the world by communities and governments to respond.

At its simplest, collective intelligence is the enhanced capacity created when distributed groups of people work together, often with the help of technology, to mobilise more information, ideas and insights to solve a problem.

Advances in digital technologies have transformed what can be achieved through collective intelligence in recent years – connecting more of us, augmenting human intelligence with machine intelligence, and helping us to generate new insights from novel sources of data. It is particularly suited to addressing fast-evolving, complex global problems such as disease outbreaks.

Here are seven ways it is tackling the coronavirus pandemic:

1. Predicting and modelling outbreaks

On the December 31, 2019, health monitoring platform Blue Dot alerted its clients to the outbreak of a flu-like virus in Wuhan, China – nine days before the World Health Organization (WHO) released a statement about it. It then correctly predicted that the virus would jump from Wuhan to Bangkok, Seoul, Taipei and Tokyo.

Blue Dot combines existing data sets to create new insights. Natural language processing, the AI methods that understand and translate human-generated text, and machine learning techniques that learn from large volumes of data, sift through reports of disease outbreaks in animals, news reports in 65 languages, and airline passenger information. It supplements the machine-generated model with human intelligence, drawing on diverse expertise from epidemiologists to veterinarians and ecologists to ensure that its conclusions are valid.

2. Citizen science

The BBC carried out a citizen science project in 2018, which involved members of the public in generating new scientific data about how infections spread. People downloaded an app that monitored their GPS position every hour, and asked them to report who they had encountered or had contact with that day….(More).

Frameworks for Collective Intelligence: A Systematic Literature Review

Curated on Posted on by Stefaan Verhulst

Paper by Shweta Suran, Vishwajeet Pattanaik, and Dirk Draheim: “Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature.

Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web….(More)”.

Mapping Wikipedia

Curated on Posted on by Stefaan Verhulst

Michael Mandiberg at The Atlantic: “Wikipedia matters. In a time of extreme political polarization, algorithmically enforced filter bubbles, and fact patterns dismissed as fake news, Wikipedia has become one of the few places where we can meet to write a shared reality. We treat it like a utility, and the U.S. and U.K. trust it about as much as the news.

But we know very little about who is writing the world’s encyclopedia. We do know that just because anyone can edit, doesn’t mean that everyone does: The site’s editors are disproportionately cis white men from the global North. We also know that, as with most of the internet, a small number of the editors do a large amount of the editing. But that’s basically it: In the interest of improving retention, the Wikimedia Foundation’s own research focuses on the motivations of people who do edit, not on those who don’t. The media, meanwhile, frequently focus on Wikipedia’s personality stories, even when covering the bigger questions. And Wikipedia’s own culture pushes back against granular data harvesting: The Wikimedia Foundation’s strong data-privacy rules guarantee users’ anonymity and limit the modes and duration of their own use of editor data.

But as part of my research in producing Print Wikipedia, I discovered a data set that can offer an entry point into the geography of Wikipedia’s contributors. Every time anyone edits Wikipedia, the software records the text added or removed, the time of the edit, and the username of the editor. (This edit history is part of Wikipedia’s ethos of radical transparency: Everyone is anonymous, and you can see what everyone is doing.) When an editor isn’t logged in with a username, the software records that user’s IP address. I parsed all of the 884 million edits to English Wikipedia to collect and geolocate the 43 million IP addresses that have edited English Wikipedia. I also counted 8.6 million username editors who have made at least one edit to an article.

The result is a set of maps that offer, for the first time, insight into where the millions of volunteer editors who build and maintain English Wikipedia’s 5 million pages are—and, maybe more important, where they aren’t….

Like the Enlightenment itself, the modern encyclopedia has a history entwined with colonialism. Encyclopédie aimed to collect and disseminate all the world’s knowledge—but in the end, it could not escape the biases of its colonial context. Likewise, Napoleon’s Description de l’Égypte augmented an imperial military campaign with a purportedly objective study of the nation, which was itself an additional form of conquest. If Wikipedia wants to break from the past and truly live up to its goal to compile the sum of all human knowledge, it requires the whole world’s participation….(More)”.

Wisdom or Madness? Comparing Crowds with Expert Evaluation in Funding the Arts

Curated on Posted on by Stefaan Verhulst

Paper by Ethan R. Mollick and Ramana Nanda: “In fields as diverse as technology entrepreneurship and the arts, crowds of interested stakeholders are increasingly responsible for deciding which innovations to fund, a privilege that was previously reserved for a few experts, such as venture capitalists and grant‐making bodies. Little is known about the degree to which the crowd differs from experts in judging which ideas to fund, and, indeed, whether the crowd is even rational in making funding decisions. Drawing on a panel of national experts and comprehensive data from the largest crowdfunding site, we examine funding decisions for proposed theater projects, a category where expert and crowd preferences might be expected to differ greatly.

We instead find significant agreement between the funding decisions of crowds and experts. Where crowds and experts disagree, it is far more likely to be a case where the crowd is willing to fund projects that experts may not. Examining the outcomes of these projects, we find no quantitative or qualitative differences between projects funded by the crowd alone, and those that were selected by both the crowd and experts. Our findings suggest that crowdfunding can play an important role in complementing expert decisions, particularly in sectors where the crowds are end users, by allowing projects the option to receive multiple evaluations and thereby lowering the incidence of “false negatives.”…(More)”.

Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India

Curated on Posted on by Stefaan Verhulst

Paper by Virgilio Galdo, Yue Li and Martin Rama: “This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample.

The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with ?other towns? and ?census towns? being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues….(More)”.

The Future of Minds and Machines

Curated on Posted on by Stefaan Verhulst

Report by Aleksandra Berditchevskaia and Peter Baek: “When it comes to artificial intelligence (AI), the dominant media narratives often end up taking one of two opposing stances: AI is the saviour or the villain. Whether it is presented as the technology responsible for killer robots and mass job displacement or the one curing all disease and halting the climate crisis, it seems clear that AI will be a defining feature of our future society. However, these visions leave little room for nuance and informed public debate. They also help propel the typical trajectory followed by emerging technologies; with inevitable regularity we observe the ascent of new technologies to the peak of inflated expectations they will not be able to fulfil, before dooming them to a period languishing in the trough of disillusionment.[1]

There is an alternative vision for the future of AI development. By starting with people first, we can introduce new technologies into our lives in a more deliberate and less disruptive way. Clearly defining the problems we want to address and focusing on solutions that result in the most collective benefit can lead us towards a better relationship between machine and human intelligence. By considering AI in the context of large-scale participatory projects across areas such as citizen science, crowdsourcing and participatory digital democracy, we can both amplify what it is possible to achieve through collective effort and shape the future trajectory of machine intelligence. We call this 21st-century collective intelligence (CI).

In The Future of Minds and Machines we introduce an emerging framework for thinking about how groups of people interface with AI and map out the different ways that AI can add value to collective human intelligence and vice versa. The framework has, in large part, been developed through analysis of inspiring projects and organisations that are testing out opportunities for combining AI & CI in areas ranging from farming to monitoring human rights violations. Bringing together these two fields is not easy. The design tensions identified through our research highlight the challenges of navigating this opportunity and selecting the criteria that public sector decision-makers should consider in order to make the most of solving problems with both minds and machines….(More)”.