Stefaan Verhulst
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)”.
Essay by Yasodara Córdova and Tiago Peixoto: “According to the World Bank’s Digital Dividends report, fewer than 20 percent of digital government projects are successes. Particularly in developing countries, these numbers are often associated with a number of challenges: limited funding, stretched implementation capacity, and political instability, to name a few. Yet, even in developing countries, despite similar conditions, some projects seem to fare better than others. Why is that?
The projects we have worked with in the global south have followed a similar pattern. While there were successes, many projects have failed. We have learned a few things along the way, that we think relate directly to the success or failure of digital government projects. These are not scientific conclusions, they’re personal impressions based on what we’ve seen and experienced.
1. Information first, services afterwards
A basic function of digital government is the provision of actionable information concerning public services, by they online or offline (e.g. opening hours, documents required for services, and so on). Even more so in developing countries, where most public services are in-person, paper-based, and often involve multiple steps. Yet, fueled by international rankings and benchmarks, governments are often eager to skip stages in their digital journey. This leads them to attempt, and often fail, to provide transactional digital services, before they can even learn how to offer basic information about these services. The first step in effective transformation should be offering information to users in a simple and accessible manner. Done well, that forms a good foundation for the next step: delivering digital services.
2. Prioritise the things that will make the biggest difference
Remember that public service delivery follows a power law distribution: a small number of services account for the vast majority of transactions with government. Which these services are will vary according to country, level of government, and models of public service delivery. When the time comes to decide where to start, don’t rely on cookie-cutter lists of services to be digitized. Instead, find out which ones are the most used, and will have the greatest impact. Start with the ones that can be delivered faster, and that are most likely to make users’ lives easier.
3. Don’t digitise the mess
The fact that a process exists doesn’t mean it’s a good process. Transformation is an opportunity to radically rethink how things work. We’ve seen examples including, for instance, requiring multiple copies of a single document, or imposing more procedures on women than men to open a business. When there is inefficiency in a service, map the bottlenecks and think about how to streamline the process. Don’t just digitise the bottlenecks, they will keep on being an expensive problem. Resist the temptation to digitise things that should not exist in the first place. …(More)”.
Open access book by Matthias C. Kettemann: “There is order on the internet, but how has this order emerged and what challenges will threaten and shape its future? This study shows how a legitimate order of norms has emerged online, through both national and international legal systems. It establishes the emergence of a normative order of the internet, an order which explains and justifies processes of online rule and regulation. This order integrates norms at three different levels (regional, national, international), of two types (privately and publicly authored), and of different character (from ius cogens to technical standards).
Matthias C. Kettemann assesses their internal coherence, their consonance with other order norms and their consistency with the order’s finality. The normative order of the internet is based on and produces a liquefied system characterized by self-learning normativity. In light of the importance of the socio-communicative online space, this is a book for anyone interested in understanding the contemporary development of the internet….(More)”.
Editorial by Karamjit S. Gill at AI&Society: “Reflecting on the rise of instrumentalism, we learn how it has travelled across the academic boundary to the high-tech culture of Silicon Valley. At its core lies the prediction paradigm. Under the cloak of inevitability of technology, we are being offered the prediction paradigm as the technological dream of public safety, national security, fraud detection, and even disease control and diagnosis. For example, there are offers of facial recognition systems for predicting behaviour of citizens, offers of surveillance drones for ’biometric readings’, ‘Predictive Policing’ is offered as an effective tool to predict and reduce crime rates. A recent critical review of the prediction technology (Coalition for Critical Technology 2020), brings to our notice the discriminatory consequences of predicting “criminality” using biometric and/or criminal legal data.
The review outlines the specific ways crime prediction technology reproduces, naturalizes and amplifies discriminatory outcomes, and why exclusively technical criteria are insufficient for evaluating their risks. We learn that neither predication architectures nor machine learning programs are neutral, they often uncritically inherit, accept and incorporate dominant cultural and belief systems, which are then normalised. For example, “Predictions” based on finding correlations between facial features and criminality are accepted as valid, interpreted as the product of intelligent and “objective” technical assessments. Furthermore, the data from predictive outcomes and recommendations are fed back into the system, thereby reproducing and confirming biased correlations. The consequence of this feedback loop, especially in facial recognition architectures, combined with a belief in “evidence based” diagnosis, is that it leads to ‘widespread mischaracterizations of criminal justice data’ that ‘justifies the exclusion and repression of marginalized populations through the construction of “risky” or “deviant” profiles’…(More).
Essay by Stefania Milan and Emiliano Treré: “Quantification is central to the narration of the COVID-19 pandemic. Numbers determine the existence of the problem and affect our ability to care and contribute to relief efforts. Yet many communities at the margins, including many areas of the Global South, are virtually absent from this number-based narration of the pandemic. This essay builds on critical data studies to warn against the universalization of problems, narratives, and responses to the virus. To this end, it explores two types of data gaps and the corresponding “data poor.” The first gap concerns the data poverty perduring in low-income countries and jeopardizing their ability to adequately respond to the pandemic. The second affects vulnerable populations within a variety of geopolitical and socio-political contexts, whereby data poverty constitutes a dangerous form of invisibility which perpetuates various forms of inequality. But, even during the pandemic, the disempowered manage to create innovative forms of solidarity from below that partially mitigate the negative effects of their invisibility….(More)”.
Book edited by edited by Linnet Taylor, Aaron Martin, Gargi Sharma and Shazade Jameson: “In early 2020, as the COVID-19 pandemic swept the world and states of emergency were declared by one country after another, the global technology sector—already equipped with unprecedented wealth, power, and influence—mobilised to seize the opportunity. This collection is an account of what happened next and captures the emergent conflicts and responses around the world. The essays provide a global perspective on the implications of these developments for justice: they make it possible to compare how the intersection of state and corporate power—and the way that power is targeted and exercised—confronts, and invites resistance from, civil society in countries worldwide.
This edited volume captures the technological response to the pandemic in 33 countries, accompanied by nine thematic reflections, and reflects the unfolding of the first wave of the pandemic.
This book can be read as a guide to the landscape of technologies deployed during the pandemic and also be used to compare individual country strategies. It will prove useful as a tool for teaching and learning in various disciplines and as a reference point for activists and analysts interested in issues of data justice.
The essays interrogate these technologies and the political, legal, and regulatory structures that determine how they are applied. In doing so,the book exposes the workings of state technological power to critical assessment and contestation….(More)”
Kaiser Health News: “After terrorists slammed a plane into the Pentagon on 9/11, ambulances rushed scores of the injured to community hospitals, but only three of the patients were taken to specialized trauma wards. The reason: The hospitals and ambulances had no real-time information-sharing system.
Nineteen years later, there is still no national data network that enables the health system to respond effectively to disasters and disease outbreaks. Many doctors and nurses must fill out paper forms on COVID-19 cases and available beds and fax them to public health agencies, causing critical delays in care and hampering the effort to track and block the spread of the coronavirus.
There are signs the COVID-19 pandemic has created momentum to modernize the nation’s creaky, fragmented public health data system, in which nearly 3,000 local, state and federal health departments set their own reporting rules and vary greatly in their ability to send and receive data electronically.
Sutter Health and UC Davis Health, along with nearly 30 other provider organizations around the country, recently launched a collaborative effort to speed and improve the sharing of clinical data on individual COVID cases with public health departments.
But even that platform, which contains information about patients’ diagnoses and response to treatments, doesn’t yet include data on the availability of hospital beds, intensive care units or supplies needed for a seamless pandemic response.
The federal government spent nearly $40 billion over the past decade to equip hospitals and physicians’ offices with electronic health record systems for improving treatment of individual patients. But no comparable effort has emerged to build an effective system for quickly moving information on infectious disease from providers to public health agencies.
In March, Congress approved $500 million over 10 years to modernize the public health data infrastructure. But the amount falls far short of what’s needed to update data systems and train staff at local and state health departments, said Brian Dixon, director of public health informatics at the Regenstrief Institute in Indianapolis….(More)”.
Case Notes by Mitchell B. Weiss and Sarah Mehta: “By April 7, 2020, over 1.4 million people worldwide had contracted the novel coronavirus (COVID-19). Governments raced to curb the spread of COVID-19 by scaling up testing, quarantining those infected, and tracing their possible contacts. It had taken Singapore’s Government Technology Agency (GovTech) and Ministry of Health (MOH) all of eight weeks to develop the world’s first nationwide deployment of a Bluetooth-based contact tracing system, TraceTogether, and deploy it in an attempt to slow the spread of COVID-19. From late January to mid-March 2020, GovTech’s Jason Bay and his team raced to create a technology that would supplement the work of Singapore’s human contact tracers. Days after its launch, Singapore’s foreign minister announced plans to open source the technology. Now, in early April, TraceTogether was a beta for the world. Whether the system would really help in Singapore, and whether other countries should adopt it was still a wide-open question….(More)”.
OECD paper by Craig Matasick, Carlotta Alfonsi and Alessandro Bellantoni: “This paper provides a holistic policy approach to the challenge of disinformation by exploring a range of governance responses that rest on the open government principles of transparency, integrity, accountability and stakeholder participation. It offers an analysis of the significant changes that are affecting media and information ecosystems, chief among them the growth of digital platforms. Drawing on the implications of this changing landscape, the paper focuses on four policy areas of intervention: public communication for a better dialogue between government and citizens; direct responses to identify and combat disinformation; legal and regulatory policy; and media and civic responses that support better information ecosystems. The paper concludes with proposed steps the OECD can take to build evidence and support policy in this space…(More)”.
Lauren Gardner, Jeremy Ratcliff, Ensheng Dong and Aaron Katz at the Lancet: “The disjointed public health response to the COVID-19 pandemic has demonstrated one clear truth: the value of timely, publicly available data. The John Hopkins University (JHU) Center for Systems Science and Engineering’s COVID-19 dashboard exists to provide this information. What grew from a modest effort to track a novel cause of pneumonia in China quickly became a mainstay symbol of the pandemic, receiving over 1 billion hits per day within weeks of its creation, primarily driven by the general public seeking information on the emerging health crisis. Critically, the data supporting the visualisation were provided in a publicly accessible repository and eagerly adopted by policy makers and the research community for purposes of modelling and planning, as evidenced by the more than 1200 citations in the first 4 months of its publication. 6 months into the pandemic, the JHU COVID-19 dashboard still stands as the authoritative source of global COVID-19 epidemiological data.
Similar commendable efforts to facilitate public understanding of COVID-19 have since been introduced by various academic, industry, and public health entities. These costly and disparate efforts around the world were necessary to fill the gap left by the lack of an established infrastructure for real-time reporting and open data sharing during an ongoing public health crisis…
Although existing systems were in place to achieve such objectives, they were not empowered or equipped to fully meet the public’s expectation for timely open data at an actionable level of spatial resolution. Moving forward, it is imperative that a standardised reporting system for systematically collecting, visualising, and sharing high-quality data on emerging infectious and notifiable diseases in real-time is established. The data should be made available at a spatial and temporal scale that is granular enough to prove useful for planning and modelling purposes. Additionally, a critical component of the proposed system is the democratisation of data; all collected information (observing necessary privacy standards) should be made publicly available immediately upon release, in machine-readable formats, and based on open data standards..(More)”. (See also https://data4covid19.org/)