Stefaan Verhulst

Report by Craig Matasick: “…innovative new set of citizen engagement practices—collectively known as deliberative democracy—offers important lessons that, when applied to the media development efforts, can help improve media assistance efforts and strengthen independent media environments around the world. At a time when disinformation runs rampant, it is more important than ever to strengthen public demand for credible information, reduce political polarization, and prevent media capture. Deliberative democracy approaches can help tackle these issues by expanding the number and diversity of voices that participate in policymaking, thereby fostering greater collective action and enhancing public support for media reform efforts.

Through a series of five illustrative case studies, the report demonstrates how deliberative democracy practices can be employed in both media development and democracy assistance efforts, particularly in the Global South. Such initiatives produce recommendations that take into account a plurality of voices while building trust between citizens and decision-makers by demonstrating to participants that their issues will be heard and addressed. Ultimately, this process can enable media development funders and practitioners to identify priorities and design locally relevant projects that have a higher likelihood for long-term impact.

– Deliberative democracy approaches, which are characterized by representative participation and moderated deliberation, provide a framework to generate demand-driven media development interventions while at the same time building greater public support for media reform efforts.

– Deliberative democracy initiatives foster collaboration across different segments of society, building trust in democratic institutions, combatting polarization, and avoiding elite capture.

– When employed by news organizations, deliberative approaches provide a better understanding of the issues their audiences care most about and uncover new problems affecting citizens that might not otherwise have come to light….(More)”.

Khari Johnson at Venture Beat: “Amsterdam and Helsinki today launched AI registries to detail how each city government uses algorithms to deliver services, some of the first major cities in the world to do so. An AI Register for each city was introduced in beta today as part of the Next Generation Internet Policy Summit, organized in part by the European Commission and the city of Amsterdam. The Amsterdam registry currently features a handful of algorithms, but it will be extended to include all algorithms following the collection of feedback at the virtual conference to lay out a European vision of the future of the internet, according to a city official.

Each algorithm cited in the registry lists datasets used to train a model, a description of how an algorithm is used, how humans utilize the prediction, and how algorithms were assessed for potential bias or risks. The registry also provides citizens a way to give feedback on algorithms their local government uses and the name, city department, and contact information for the person responsible for the responsible deployment of a particular algorithm. A complete algorithmic registry can empower citizens and give them a way to evaluate, examine, or question governments’ applications of AI.

In a previous development in the U.S., New York City created an automated decision systems task force in 2017 to document and assess city use of algorithms. At the time it was the first city in the U.S. to do so. However, following the release of a report last year, commissioners on the task force complained about a lack of transparency and inability to access information about algorithms used by city government agencies….

In a statement accompanying the announcement, Helsinki City Data project manager Pasi Rautio said the registry is also aimed at increasing public trust in the kinds of artificial intelligence “with the greatest possible openness.”…(More)”.

Jos Berens at Bloomberg New Economy Forum: “…Despite these and other examples, data sharing between the private sector and humanitarian agencies is still limited. Out of 281 contributing organizations on HDX, only a handful come from the private sector. 

So why don’t we see more use of private sector data in humanitarian response? One obvious set of challenges concerns privacy, data protection and ethics. Companies and their customers are often wary of data being used in ways not related to the original purpose of data collection. Such concerns are understandable, especially given the potential legal and reputational consequences of personal data breaches and leaks.

Figuring out how to use this type of sensitive data in an already volatile setting seems problematic, and it is — negotiations between public and private partners in the middle of a crisis often get hung up on a lack of mutual understanding. Data sharing partnerships negotiated during emergencies often fail to mature beyond the design phase. This dynamic creates a loop of inaction due to a lack of urgency in between crises, followed by slow and halfway efforts when action is needed most.

To ensure that private sector data is accessible in an emergency, humanitarian organizations and private sector companies need to work together to build partnerships before a crisis. They can do this by taking the following actions: 

• Invest in relationships and build trust. Both humanitarian organizations and private sector organizations should designate focal points who can quickly identify potentially useful data during a humanitarian emergency. A data stewards network which identifies and connects data responsibility leaders across organizations, as proposed by the NYU Govlab, is a great example of how such relations could look. Efforts to build trust with the general public regarding private sector data use for humanitarian response should also be strengthened, primarily through transparency about the means and purpose of such collaborations. This is particularly important in the context of COVID-19, as noted in the UN Comprehensive Response to COVID-19 and the World Economic Forum’s ‘Great Reset’ initiative…(More)”.

Book by Angèle Christin: “When the news moved online, journalists suddenly learned what their audiences actually liked, through algorithmic technologies that scrutinize web traffic and activity. Has this advent of audience metrics changed journalists’ work practices and professional identities? In Metrics at Work, Angèle Christin documents the ways that journalists grapple with audience data in the form of clicks, and analyzes how new forms of clickbait journalism travel across national borders.

Drawing on four years of fieldwork in web newsrooms in the United States and France, including more than one hundred interviews with journalists, Christin reveals many similarities among the media groups examined—their editorial goals, technological tools, and even office furniture. Yet she uncovers crucial and paradoxical differences in how American and French journalists understand audience analytics and how these affect the news produced in each country. American journalists routinely disregard traffic numbers and primarily rely on the opinion of their peers to define journalistic quality. Meanwhile, French journalists fixate on internet traffic and view these numbers as a sign of their resonance in the public sphere. Christin offers cultural and historical explanations for these disparities, arguing that distinct journalistic traditions structure how journalists make sense of digital measurements in the two countries.

Contrary to the popular belief that analytics and algorithms are globally homogenizing forces, Metrics at Work shows that computational technologies can have surprisingly divergent ramifications for work and organizations worldwide….(More)”.

Matthias Daub, Tony D’Emidio, Zaana Howard, and Seckin Ungur at McKinsey: “Who knew that one could develop warm feelings for a German Federal Employment Agency chatbot? If you own a business and wish to apply for state funds to supplement your employees’ reduced salaries, then UDO will fill in the application form for you. “Let’s go!” the digital assistant declares, launching into a series of questions. The system displays reassuring expertise; the queries—about the size of your workforce, the extent of the reduction in working hours, and so on—are simple, clear, and sensitive to previous responses, and the interface offers soothing blue tones and rounded edges. UDO goes on to ask why the workers are on reduced hours: for economic reasons, such as the cancellation of a large order due to the coronavirus, or because of an unavoidable event, such as a measure to mitigate the spread of the pandemic? And by now, a powerful and comforting thought may well arise in the citizen’s mind: UDO really cares.

In this article, we argue that smart use of automation can enable governments to provide outstanding levels of customer experience, driven by innovations that are as sensitive to people as they are to technology. We begin by considering the challenges and rewards of enhancing customer experience for governments. Then we discuss the benefits to governments of using automation to improve customer experience. Finally, we turn from why to how, identifying three key practices common to successful automation initiatives in public services….(More)”.

Mozilla: “Timely and open access to novel outputs is key to scientific research. It allows scientists to reproduce, test, and build on one another’s work — and ultimately unlock progress.

The most recent example of this is the research into COVID-19. Much of the work was published in open access journals, swiftly reviewed and ultimately improving our understanding of how to slow the spread and treat the disease. Although this rapid increase in scientific publications is evident in other domains too, we might not be reaping the benefits. The tools to parse and combine this newly created knowledge have roughly remained the same for years.

Today, Mozilla Fellow Kostas Stathoulopoulos is launching Orion — an open-source tool to illuminate the science behind the science and accelerate knowledge discovery in the life sciences. Orion enables users to monitor progress in science, visually explore the scientific landscape, and search for relevant publications.

Orion collects, enriches and analyses scientific publications in the life sciences from Microsoft Academic Graph.

Users can leverage Orion’s views to interact with the data. The Exploration view shows all of the academic publications in a three-dimensional visualization. Every particle is a paper and the distance between them signifies their semantic similarity; the closer two particles are, the more semantically similar. The Metrics view visualizes indicators of scientific progress and how they have changed over time for countries and thematic topics. The Search view enables the users to search for publications by submitting either a keyword or a longer query, for example, a sentence or a paragraph of a blog they read online….(More)”.

Article by Eliza McCullough: “….Instead of a smart city model that extracts from, surveils, and displaces poor people of color, we need a democratic model that allows community members to decide how technological infrastructure operates and to ensure the equitable distribution of benefits. Doing so will allow us to create cities defined by inclusion, shared ownership, and shared prosperity.

In 2016, Barcelona, for example, launched its Digital City Plan, which aims to empower residents with control of technology used in their communities. The document incorporates over 8,000 proposals from residents and includes plans for open source software, government ownership of all ICT infrastructure, and a pilot platform to help citizens maintain control over their personal data. As a result, the city now has free applications that allow residents to easily propose city development ideas, actively participate in city council meetings, and choose how their data is shared.

In the U.S., we need a framework for tech sovereignty that incorporates a racial equity approach: In a racist society, race neutrality facilitates continued exclusion and exploitation of people of color. Digital Justice Lab in Toronto illustrates one critical element of this kind of approach: access to information. In 2018, the organization gave community groups a series of grants to hold public events that shared resources and information about digital rights. Their collaborative approach intentionally focuses on the specific needs of people of color and other marginalized groups.

The turn toward intensified surveillance infrastructure in the midst of the coronavirus outbreak makes the need to adopt such practices all the more crucial. Democratic tech models that uplift marginalized populations provide us the chance to build a city that is just and open to everyone….(More)”.

Paper by Ciara Greene and Gillian Murphy: “Previous research has argued that fake news may have grave consequences for health behaviour, but surprisingly, no empirical data have been provided to support this assumption. This issue takes on new urgency in the context of the coronavirus pandemic. In this large preregistered study (N = 3746) we investigated the effect of exposure to fabricated news stories about COVID-19 on related behavioural intentions. We observed small but measurable effects on some related behavioural intentions but not others – for example, participants who read a story about problems with a forthcoming contact-tracing app reported reduced willingness to download the app. We found no effects of providing a general warning about the dangers of online misinformation on response to the fake stories, regardless of the framing of the warning in positive or negative terms. We conclude with a call for more empirical research on the real-world consequences of fake news….(More)”

Matthew Hutson at IEEE Spectrum: “…Researchers say they’ve learned a lot of lessons modeling this pandemic, lessons that will carry over to the next.

The first set of lessons is all about data. Garbage in, garbage out, they say. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says it’s not clear what we should be predicting. Infections, deaths, and hospitalization numbers each have problems, which affect their usefulness not only as inputs for the model but also as outputs. It’s hard to know the true number of infections when not everyone is tested. Deaths are easier to count, but they lag weeks behind infections. Hospitalization numbers have immense practical importance for planning, but not all hospitals release those figures. How useful is it to predict those numbers if you never have the true numbers for comparison? What we need, he said, is systematized random testing of the population, to provide clear statistics of both the number of people currently infected and the number of people who have antibodies against the virus, indicating recovery. Prakash, of Georgia Tech, says governments should collect and release data quickly in centralized locations. He also advocates for central repositories of policy decisions, so modelers can quickly see which areas are implementing which distancing measures.

Researchers also talked about the need for a diversity of models. At the most basic level, averaging an ensemble of forecasts improves reliability. More important, each type of model has its own uses—and pitfalls. An SEIR model is a relatively simple tool for making long-term forecasts, but the devil is in the details of its parameters: How do you set those to match real-world conditions now and into the future? Get them wrong and the model can head off into fantasyland. Data-driven models can make accurate short-term forecasts, and machine learning may be good for predicting complicated factors. But will the inscrutable computations of, for instance, a neural network remain reliable when conditions change? Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.

Finally, researchers emphasize the need for agility. Niemi of Iowa State says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says Meyers, of the University of Texas. “The pace of innovation, the pace of development, is unlike ever before,” she says. “There are new statistical methods, new kinds of data, new model structures.”…(More)”.

Poster by Geoffrey Henry Siwo: The promise of artificial intelligence (AI) in medicine is advancing rapidly driven by exponential growth in computing speed, data and new modeling techniques such as deep learning. Unfortunately, advancements in AI stand to disproportionately benefit diseases that predominantly affect the developed world because the key ingredients for AI – computational resources, big data and AI expertise – are less accessible in the developing world. Our research on automated mining of biomedical literature indicates that adoption of machine learning algorithms in global health, for example to understand malaria, lags several years behind diseases like cancer.

 To shift these inequities, we have been exploring the use of crowdsourced data science challenges as a means to rapidly advance computational models in global health. Data science challenges involve seeking computational solutions for specific, well-defined questions from anyone in the world. Here we describe key lessons from our work in this area and the potential value of data science challenges in accelerating AI for global health.

In one of our first initiatives in this area – the Malaria DREAM Challenge – we invited data scientists from across the world to develop computational models that predict the in vitro and in vivo drug sensitivity of malaria parasites to artemisinin using gene expression datasets. More than 360 individuals drawn from academia, government and startups across 31 countries participated in the challenge. Approximately 100 computational solutions to the problem were generated within a period of 3 months. In addition to this sheer volume of participation, a diverse range of modeling approaches including artificial neural networks and automated machine learning were employed….(More)”.