Paper by Miguel Arana-Catania, Felix-Anselm van Lier and Rob Procter: “Today’s conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the article also emphasizes the importance of interdisciplinary and participatory, cocreation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation…(More)”.
Academic freedom and democracy in African countries: the first study to track the connection
Article by Liisa Laakso: “There is growing interest in the state of academic freedom worldwide. A 1997 Unesco document defines it as the right of scholars to teach, discuss, research, publish, express opinions about systems and participate in academic bodies. Academic freedom is a cornerstone of education and knowledge.
Yet there is surprisingly little empirical research on the actual impact of academic freedom. Comparable measurements have also been scarce. It was only in 2020 that a worldwide index of academic freedom was launched by the Varieties of Democracy database, V-Dem, in collaboration with the Scholars at Risk Network….
My research has been on the political science discipline in African universities and its role in political developments on the continent. As part of this project, I have investigated the impact of academic freedom in the post-Cold War democratic transitions in Africa.
A study I published with the Tunisian economist Hajer Kratou showed that academic freedom has a significant positive effect on democracy, when democracy is measured by indicators such as the quality of elections and executive accountability.
However, the time factor is significant. Countries with high levels of academic freedom before and at the time of their democratic transition showed high levels of democracy even 5, 10 and 15 years later. In contrast, the political situation was more likely to deteriorate in countries where academic freedom was restricted at the time of transition. The impact of academic freedom was greatest in low-income countries….(More)”
Africa: regulate surveillance technologies and personal data
Bulelani Jili in Nature: “…For more than a decade, African governments have installed thousands of closed-circuit television (CCTV) cameras and surveillance devices across cities, along with artificial-intelligence (AI) systems for facial recognition and other uses. Such technologies are often part of state-led initiatives to reduce crime rates and strengthen national security against terrorism. For instance, in Uganda in 2019, Kampala’s police force procured digital cameras and facial-recognition technology worth US$126 million to help it address a rise in homicides and kidnappings (see go.nature.com/3nx2tfk).
However, digital surveillance tools also raise privacy concerns. Citizens, academics and activists in Kampala contend that these tools, if linked to malicious spyware and malware programs, could be used to track and target citizens. In August 2019, an investigation by The Wall Street Journal found that Ugandan intelligence officials had used spyware to penetrate encrypted communications from the political opposition leader Bobi Wine1.
Around half of African countries have laws on data protection. But these are often outdated and lack clear enforcement mechanisms and strategies for secure handling of biometric data, including face, fingerprint and voice records. Inspections, safeguards and other standards for monitoring goods and services that use information and communications technology (ICT) are necessary to address cybersecurity and privacy risks.
The African Union has begun efforts to create a continent-wide legislative framework on this topic. As of March this year, only 13 of the 55 member states have ratified its 2014 Convention on Cyber Security and Personal Data Protection; 15 countries must do so before it can take effect. Whereas nations grappling with food insecurity, conflict and inequality might not view cybersecurity as a priority, some, such as Ghana, are keen to address this vulnerability so that they can expand their information societies.
The risks of using surveillance technologies in places with inadequate laws are great, however, particularly in a region with established problems at the intersections of inequality, crime, governance, race, corruption and policing. Without robust checks and balances, I contend, such tools could encourage political repression, particularly in countries with a history of human-rights violations….(More)”.
Building a Data Infrastructure for the Bioeconomy
Article by Gopal P. Sarma and Melissa Haendel: “While the development of vaccines for COVID-19 has been widely lauded, other successful components of the national response to the pandemic have not received as much attention. The National COVID Cohort Collaborative (N3C), for example, flew under the public’s radar, even though it aggregated crucial US public health data about the new disease through cross-institutional collaborations among government, private, and nonprofit health and research organizations. These data, which were made available to researchers via cutting-edge software tools, have helped in myriad ways: they led to identification of the clinical characteristics of acute COVID-19 for risk prediction, assisted in providing clinical care for immunocompromised adults, revealed how COVID infection affects children, and documented that vaccines appear to reduce the risk of developing long COVID.
N3C has created the largest national, publicly available patient-level dataset in US history. Through a unique public-private partnership, over 300 participating organizations quickly overcame privacy concerns and data silos to include 13 million patient records in the project. More than 3,000 participating scientists are now working to overcome the particular challenge faced in the United States—the lack of a national healthcare data infrastructure available in many other countries—to support public health and medical responses. N3C shows great promise for unraveling answers to questions related to COVID, but it could easily be expanded for many areas of public health, including pandemic preparedness and monitoring disease status across the population.
As public servants dedicated to improving public health and equity, we believe that to unite the nation’s fragmented public health system, the United States should establish a standing capacity to collect, harmonize, and sustain a wide range of data types and sources. The public health data collected by N3C would ultimately be but one component of a rich landscape of interoperable data systems that can guide public policy in an era of rapid environmental change, sophisticated biological threats, and an economy enabled by biotechnology. Such an effort will require new thinking about data collection, infrastructure, and regulation, but its benefits could be enormous—enabling policymakers to make decisions in an increasingly complex world. And as the interconnections between society, industry, and government continue to intensify, decisionmaking of all types and scales will be more efficient and responsive if it can rely on significantly expanded data collection and analysis capabilities…(More)”.
Using mobile money data and call detail records to explore the risks of urban migration in Tanzania
Paper by Rosa Lavelle-Hill: “Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. Call detail records were used to identify individuals who migrated to Dar es Salaam, Tanzania’s largest city. A street survey of the city’s subwards was used to determine which individuals moved to more deprived areas. t-tests showed that people who settled in poorer neighbourhoods had less money coming into their mobile money account after they moved, but not before. A machine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation. Features indicating the strength and location of people’s social connections in Dar es Salaam before they moved (‘pull factors’) were found to be most predictive, more so than traditional ‘push factors’ such as proxies for poverty in the migrant’s source region…(More)”.
Lexota
Press Release: “Today, Global Partners Digital (GPD), the Centre for Human Rights at the University of Pretoria (CHR), Article 19 West Africa, the Collaboration on International ICT Policy in East and Southern Africa (CIPESA) and PROTEGE QV jointly launch LEXOTA—Laws on Expression Online: Tracker and Analysis, a new interactive tool to help human rights defenders track and analyse government responses to online disinformation across Sub-Saharan Africa.
Expanding on work started in 2020, LEXOTA offers a comprehensive overview of laws, policies and other government actions on disinformation in every country in Sub-Saharan Africa. The tool is powered by multilingual data and context-sensitive insight from civil society organisations and uses a detailed framework to assess whether government responses to disinformation are human rights-respecting. A dynamic comparison feature empowers users to examine the regulatory approaches of different countries and to compare how different policy responses measure up against human rights standards, providing them with insights into trends across the region as well as the option to examine country-specific analyses.
In recent years, governments in Sub-Saharan Africa have increasingly responded to disinformation through content-based restrictions and regulations, which often pose significant risks to individuals’ right to freedom of expression. LEXOTA was developed to support those working to defend internet freedom and freedom of expression across the region, by making data on these government actions accessible and comparable…(More)”.
Building Data Infrastructure in Development Contexts: Lessons from the Data4COVID19 Africa Challenge
Report by Stefaan Verhulst, Andrew Young, Andrew J. Zahuranec, Peter Martey Addo: “COVID-19 and other societal threats hamper the ability of development practitioners and stakeholders to address The COVID-19 pandemic has posed a number of unprecedented societal threats. While the effects of the crisis know no borders, the pandemic’s consequences have been felt in a particularly acute way in developing economies across the Global South. Indeed, while estimates of excess mortality show that many developing economies compare favorably to other parts of the world, the pandemic has still overburdened health systems and disrupted food supplies, increasing the risk of malnutrition. Economic estimates suggest that COVID-19 will reduce the GDP of African economies by 1.4 percent, with smaller economies facing contractions of up to 7.8 percent (Gondwe 2020).
Given that development agencies have limited resources to fight the effects of the pandemic, data can play an important role in bolstering decision-making processes. When data is available and used responsibly, it can generate important insights about what is happening, help organizations understand cause and effect, improve forecasting, and assess the impact of efforts (Verhulst et al. 2021). However, the major limiting factors are the amount of data and the expertise available in the ecosystem. These limitations are especially severe in least-developed countries, such as those in Sub-Saharan Africa. However, datadriven challenges—short-term exercises where data and expertise is brought to bear on some pressing social challenge—can be useful tools for overcoming these limiting factors by, attracting data holders and practitioners to engage in rapid action to advance development goals…(More)”
Machine learning and phone data can improve targeting of humanitarian aid
Paper by Emily Aiken, Suzanne Bellue, Dean Karlan, Chris Udry & Joshua E. Blumenstock: “The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date…(More)”.
Repeat photos show change in southern African landscapes: a citizen science project
Paper by Timm Hoffman and Hana Petersen: “Every place in the world has a history. To understand it in the present you need some knowledge of its past. The history of the earth can be read from its rocks; the history of life, from the evolutionary histories and relationships of its species. But what of the history of modern landscapes and the many benefits we derive from them, such as water and food? What are their histories – and how are they shifting in response to the intense pressures they face from climate change and from people?
Historical landscape photographs provide one way of measuring this. They capture the way things were at a moment in time. By standing at the same place and re-photographing the same scene, it is possible to document the nature of change. Sometimes researchers can even measure the extent and rate of change for different elements in the landscape.
Reasons for the change can also sometimes be observed from this and other historical information, such as the climate or fire record. All of these data can then be related to what has been written about environmental change using other approaches and models. Researchers can ascertain whether the environment has reached a critical threshold and consider how to respond to the changes.
This is what repeat photography is all about…
The rePhotoSA project was launched in August 2015. The idea is to involve interested members of the public in re-photographing historical locations. This has two benefits. First, participants add to the number of repeated images. Second, public awareness of landscape change is raised.
The project website has over 6,000 historical images from ten primary photographic collections of southern African landscapes, dating from the late 1800s to the early 2000s. The geographic spread of the photographs is influenced largely by the interests of the original photographers. Often these photographs are donated to the project by family members, or institutions to which the original photographers belonged – and sometimes by the photographers themselves….(More)
How data can help migrants
Blog by Andrew Young: “…Actors across sectors are experimenting with new data innovations to improve decision-making on migration and fill gaps in official statistics and traditional data sources. Non-traditional data, including privately held information, can complement traditional data sources that are not always timely or sufficient. Innovative uses of data can help us forecast and understand macro-level trends and developments in migration flows and the drivers of these phenomena, such as labour market disruptions. They can also support a better understanding of migrants’ experience, through more demographically-disaggregated information and more insight into “data invisibles” who are not represented in official statistics.
Specifically, new forms of data collaboration are enabling the use of data from telecoms, social media companies and satellite imagery to enhance civil registration procedures for migrants, forecast the effects of sea level rises on migration and nowcast international migration flows, for example. The Big Data for Migration Alliance (BD4M) was established to accelerate the responsible and ethical use of non-traditional data sources and methods. The BD4M is experimenting with new co-design and prototyping methods to tap into global expertise and advance more responsible and effective data collaboration to support data innovations for migration. The first of these “studios” investigated how to design data collaboration to better understand human mobility and migration in West Africa, including by leveraging non-traditional data.
Actors face persistent challenges in advancing innovative uses of non-traditional data to improve migration policymaking while also providing greater autonomy and agency to migrants at key moments of the data life cycle. It is a task that spans initial data collection, data processing, sharing, analysis and (re)use of data. However, more research and evidence is needed to advance digital self-determination in a way that respectfully empowers data subjects, including migrants.
The recently established International Network on Digital Self Determination (IDSD), an interdisciplinary consortium studying and designing ways to engage in trustworthy data spaces and ensure human centric approaches, is spearheading this work. The IDSD is also promoting and facilitating the use of collaborative studios to convene domain experts and migrants to define strategies that make sure that the data subjects themselves are aware of emerging uses of data that concerns them and are positioned to influence the design and objectives of new data innovations. By tapping into migrants’ perspectives, actors can ensure their data collaboration efforts are aligned with the priorities of their intended beneficiaries and conduct their work with the type of clear social license that is often lacking in the space….(More)”.