Press Release: “Today, Global Partners Digital (GPD), ARTICLE 19, the Collaboration on International ICT Policy for East and Southern Africa (CIPESA), PROTEGE QV and the Centre for Human Rights of the University of Pretoria jointly launched an interactive map to track and analyse disinformation laws, policies and patterns of enforcement across Sub-Saharan Africa.
The map offers a birds-eye view of trends in state responses to disinformation across the region, as well as in-depth analysis of the state of play in individual countries, using a bespoke framework to assess whether laws, policies and other state responses are human rights-respecting.
Developed against a backdrop of rapidly accelerating state action on COVID-19 related disinformation, the map is an open, iterative product. At the time of launch, it covers 31 countries (see below for the full list), with an aim to expand this in the coming months. All data, analysis and insight on the map has been generated by groups and actors based in Africa….(More)”.
Andrew Jack at the Financial Times: “When Mozambique was hit by two cyclones in rapid succession last year — causing death and destruction from a natural disaster on a scale not seen in Africa for a generation — government officials added an unusual recruit to their relief efforts. Apart from the usual humanitarian and health agencies, the National Health Institute also turned to Zenysis, a Silicon Valley start-up.
As the UN and non-governmental organisations helped to rebuild lives and tackle outbreaks of disease including cholera, Zenysis began gathering and analysing large volumes of disparate data. “When we arrived, there were 400 new cases of cholera a day and they were doubling every 24 hours,” says Jonathan Stambolis, the company’s chief executive. “None of the data was shared [between agencies]. Our software harmonised and integrated fragmented sources to produce a coherent picture of the outbreak, the health system’s ability to respond and the resources available.
“Three and a half weeks later, they were able to get infections down to zero in most affected provinces,” he adds. The government attributed that achievement to the availability of high-quality data to brief the public and international partners.
“They co-ordinated the response in a way that drove infections down,” he says. Zenysis formed part of a “virtual control room”, integrating information to help decision makers understand what was happening in the worst hit areas, identify sources of water contamination and where to prioritise cholera vaccinations.
It supported an “mAlert system”, which integrated health surveillance data into a single platform for analysis. The output was daily reports distilled from data issued by health facilities and accommodation centres in affected areas, disease monitoring and surveillance from laboratory testing….(More)”.
Paper by Peter John and Fredrik M. Sjoberg: “Citizens respond to information about democracy according to whether they are electoral winners or losers. This difference occurs both at the national and constituency level. Democratic interventions that seek to promote accountability and transparency might therefore impact citizens differentially depending on the political party that people support. In a placebo-controlled experimental design, carried out in Kenya, we find that democracy promotion boosts the external efficacy and political participation of ruling party partisans, but leaves those from the opposition unaffected. These responses—based on national incumbency—are further conditioned by the partisanship of the MP of the constituency where the voter resides. These findings throw new light on the impact of civic interventions, such as Get Out the Vote (GOTV) and civic education, common in Africa as well as elsewhere, as we show their benefits accrue to the electoral winners rather than to the losers…(More)”.
Paper by Megumi Kubota and Albert Zeufack: “This paper investigates the potential benefits for a country from investing in data transparency. The paper shows that increased data transparency can bring substantive returns in lower costs of external borrowing.
This result is obtained by estimating the impact of public data transparency on sovereign spreads conditional on the country’s level of institutional quality and public and external debt. While improving data transparency alone reduces the external borrowing costs for a country, the return is much higher when combined with stronger institutional quality and lower public and external debt. Similarly, the returns on investing in data transparency are higher when a country’s integration to the global economy deepens, as captured by trade and financial openness.
Estimation of an instrumental variable regression shows that Sub-Saharan African countries could have saved up to 14.5 basis points in sovereign bond spreads and decreased their external debt burden by US$405.4 million (0.02 percent of gross domestic product) in 2018, if their average level of data transparency was that of a country in the top quartile of the upper-middle-income country category. At the country level, Angola could have reduced its external debt burden by around US$73.6 million….(More)”.
Abby Sewell at Wired: “On the outskirts of Zahle, a town in Lebanon’s Beqaa Valley, a pair of aid workers carrying clipboards and cell phones walk through a small refugee camp, home to 11 makeshift shelters built from wood and tarps.
A camp resident leading them through the settlement—one of many in the Beqaa, a wide agricultural plain between Beirut and Damascus with scattered villages of cinderblock houses—points out a tent being renovated for the winter. He leads them into the kitchen of another tent, highlighting cracking wood supports and leaks in the ceiling. The aid workers record the number of residents in each tent, as well as the number of latrines and kitchens in the settlement.
The visit is part of an initiative by the Switzerland-based NGO Medair to map the locations of the thousands of informal refugee settlements in Lebanon, a country where even many city buildings have no street addresses, much less tents on a dusty country road.
“I always say that this project is giving an address to people that lost their home, which is giving back part of their dignity in a way,” says Reine Hanna, Medair’s information management project manager, who helped develop the mapping project.
The initiative relies on GIS technology, though the raw data is collected the old-school way, without high tech mapping aids like drones. Mapping teams criss-cross the country year round, stopping at each camp to speak to residents and conduct a survey. They enter the coordinates of new camps or changes in the population or facilities of old ones into a database that’s shared with UNHCR, the UN refugee agency, and other NGOs working in the camps. The maps can be accessed via a mobile app by workers heading to the field to distribute aid or respond to emergencies.
Lebanon, a small country with an estimated native population of about 4 million, hosts more than 900,000 registered Syrian refugees and potentially hundreds of thousands more unregistered, making it the country with the highest population of refugees per capita in the world.
But there are no official refugee camps run by the government or the UN refugee agency in Lebanon, where refugees are a sensitive subject. The country is not a signatory to the 1951 Refugee Convention, and government officials refer to the Syrians as “displaced,” not “refugees.”
Lebanese officials have been wary of the Syrians settling permanently, as Palestinian refugees did beginning in 1948. Today, more than 70 years later, there are some 470,000 Palestinian refugees registered in Lebanon, though the number living in the country is believed to be much lower….(More)”.
Youssef Travaly and Kevin Muvunyi at Brookings: “…AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.
In agriculture, Abdoulaye Baniré Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.
However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africa’s digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.
Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market….(More)”.
Ulises Ali Mejias at AlJazeera: “The recent coup in Bolivia reminds us that poor countries rich in resources continue to be plagued by the legacy of colonialism. Anything that stands in the way of a foreign corporation’s ability to extract cheap resources must be removed.
Today, apart from minerals and fossil fuels, corporations are after another precious resource: Personal data. As with natural resources, data too has become the target of extractive corporate practices.
As sociologist Nick Couldry and I argue in our book, The Costs of Connection: How Data is Colonizing Human Life and Appropriating It for Capitalism, there is a new form of colonialism emerging in the world: data colonialism. By this, we mean a new resource-grab whereby human life itself has become a direct input into economic production in the form of extracted data.
We acknowledge that this term is controversial, given the extreme physical violence and structures of racism that historical colonialism employed. However, our point is not to say that data colonialism is the same as historical colonialism, but rather to suggest that it shares the same core function: extraction, exploitation, and dispossession.
Like classical colonialism, data colonialism violently reconfigures human relations to economic production. Things like land, water, and other natural resources were valued by native people in the precolonial era, but not in the same way that colonisers (and later, capitalists) came to value them: as private property. Likewise, we are experiencing a situation in which things that were once primarily outside the economic realm – things like our most intimate social interactions with friends and family, or our medical records – have now been commodified and made part of an economic cycle of data extraction that benefits a few corporations.
So what could countries in the Global South do to avoid the dangers of data colonialism?…(More)”.
Report by Andrej Verity and Irene Solaiman: “Data collection and storage are becoming increasingly digital. In the humanitarian sector, data motivates action, informing organizations who then determine priorities and resource allocation in crises.
“Humanitarians are dependent on technology and on the Internet. When life-saving aid isn’t delivered on time and to the right beneficiaries, people can die.” -Brookings
In the age of information and cyber warfare, humanitarian organizations must take measures to protect civilians, especially those in critical and vulnerable positions.
“Data privacy and ensuring protection from harm, including the provision of data security, are therefore fundamentally linked—and neither can be realized without the other.” -The Signal Code
Information in the wrong hands can risk lives or even force aid organizations to shut down. For example, in 2009, Sudan expelled over a dozen international nongovernmental organizations (NGOs) that were deemed key to maintaining a lifeline to 4.7 million people in western Darfur. The expulsion occurred after the Sudanese Government collected Internet-accessible information that made leadership fear international criminal charges. Responsible data protection is a crucial component of cybersecurity. As technology develops, so do threats and data vulnerabilities. Emerging technologies such as blockchain provide further security to sensitive information and overall data storage. Still, with new technologies come considerations for implementation…(More)”.
Blogpost by Damien Jacques et al: “Cashew nuts in Burkina Faso can be seen growing from space. Such is the power of satellite technology, it’s now possible to observe the changing colors of fields as crops slowly ripen.
This matters because it can be used as an early warning of crop failure and food crisis – giving governments and aid agencies more time to organize a response.
Our team built an exhaustive crop type and yield estimation map in Burkina Faso, using artificial intelligence and satellite images from the European Space Agency.
But building the map would not have been possible without a data set that GIZ, the German government’s international development agency, had collected for one purpose on the ground some years before – and never looked at again.
At Dalberg, we call this a “hidden data treasury” and it has huge potential to be used for good.
Unlocking data potential
In the records of the GIZ Data Lab, the GPS coordinates and crop yield measurements of just a few hundred cashew fields were sitting dormant.
They’d been collected in 2015 to assess the impact of a program to train farmers. But through the power of machine learning, that data set has been given a new purpose.
Using Dalberg Data Insights’ AIDA platform, our team trained algorithms to analyze satellite images for cashew crops, track the crops’ color as they ripen, and from there, estimate yields for the area covered by the data.
From this, it’s now possible to predict crop failures for thousands of fields.
We believe this “recycling” of old data, when paired with artificial intelligence, can help to bridge the data gaps in low-income countries and meet the UN’s Sustainable Development Goals….(More)”.
Seán Mfundza Muller, Grieve Chelwa, and Nimi Hoffmann at the Conversation: “…One view of the challenge of development is that it is fundamentally about answering causal questions. If a country adopts a particular policy, will that cause an increase in economic growth, a reduction in poverty or some other improvement in the well-being of citizens?
In recent decades economists have been concerned about the reliability of previously used methods for identifying causal relationships. In addition to those methodological concerns, some have argued that “grand theories of development” are either incorrect or at least have failed to yield meaningful improvements in many developing countries.
Two notable examples are the idea that developing countries may be caught in a poverty trap that requires a “big push” to escape and the view that institutions are key for growth and development.
These concerns about methods and policies provided a fertile ground for randomised experiments in development economics. The surge of interest in experimental approaches in economics began in the early 1990s. Researchers began to use “natural experiments”, where for example random variation was part of a policy rather than decided by a researcher, to look at causation.
But it really gathered momentum in the 2000s, with researchers such as the Nobel awardees designing and implementing experiments to study a wide range of microeconomic questions.
Proponents of these methods argued that a focus on “small” problems was more likely to succeed. They also argued that randomised experiments would bring credibility to economic analysis by providing a simple solution to causal questions.
These experiments randomly allocate a treatment to some members of a group and compare the outcomes against the other members who did not receive treatment. For example, to test whether providing credit helps to grow small firms or increase their likelihood of success, a researcher might partner with a financial institution and randomly allocate credit to applicants that meet certain basic requirements. Then a year later the researcher would compare changes in sales or employment in small firms that received the credit to those that did not.
Randomised trials are not a new research method. They are best known for their use in testing new medicines. The first medical experiment to use controlled randomisation occurred in the aftermath of the second world war. The British government used it to assess the effectiveness of a drug for tuberculosis treatment.
In the early 20th century and mid-20th century American researchers had used experiments like this to examine the effects of various social policies. Examples included income protection and social housing.
The introduction of these methods into development economics also followed an increase in their use in other areas of economics. One example was the study of labour markets.
The popularity, among academics and policymakers, of the approach is not only due to its seeming ability to solve methodological and policy concerns. It is also due to very deliberate, well-funded advocacy by its proponents….(More)”.