Leading, not lagging: Africa’s gen AI opportunity

Curated on Posted on by Stefaan Verhulst

Article by Mayowa Kuyoro, Umar Bagus: “The rapid rise of gen AI has captured the world’s imagination and accelerated the integration of AI into the global economy and the lives of people across the world. Gen AI heralds a step change in productivity. As institutions apply AI in novel ways, beyond the advanced analytics and machine learning (ML) applications of the past ten years, the global economy could increase significantly, improving the lives and livelihoods of millions.1

Nowhere is this truer than in Africa, a continent that has already demonstrated its ability to use technology to leapfrog traditional development pathways; for example, mobile technology overcoming the fixed-line internet gap, mobile payments in Kenya, and numerous African institutions making the leap to cloud faster than their peers in developed markets.2 Africa has been quick on the uptake with gen AI, too, with many unique and ingenious applications and deployments well underway…(More)”.

Across McKinsey’s client service work in Africa, many institutions have tested and deployed AI solutions. Our research has found that more than 40 percent of institutions have either started to experiment with gen AI or have already implemented significant solutions (see sidebar “About the research inputs”). However, the continent has so far only scratched the surface of what is possible, with both AI and gen AI. If institutions can address barriers and focus on building for scale, our analysis suggests African economies could unlock up to $100 billion in annual economic value across multiple sectors from gen AI alone. That is in addition to the still-untapped potential from traditional AI and ML in many sectors today—the combined traditional AI and gen AI total is more than double what gen AI can unlock on its own, with traditional AI making up at least 60 percent of the value…(More)”

Activating citizens: the contribution of the Capability Approach to critical citizenship studies and to understanding the enablers of engaged citizenship

Curated on Posted on by Stefaan Verhulst

Paper by Anna Colom and Agnes Czajka: “The paper argues that the Capability Approach can make a significant contribution to understanding the enablers of engaged citizenship. Using insights from critical citizenship studies and original empirical research on young people’s civic and political involvement in western Kenya, we argue that it is useful to think of the process of engaged citizenship as comprised of two distinct yet interrelated parts: activation and performance. We suggest that the Capability Approach (CA) can help us understand what resources and processes are needed for people to not only become activated but to also effectively perform their citizenship. Although the CA is rarely brought into conversation with critical citizenship studies literatures, we argue that it can be useful in both operationalising the insights of critical citizenship studies on citizenship engagement and illustrating how activation and performance can be effectively supported or catalysed….(More)”

How Behaviorally-Informed Technologies Are Shaping Global Aid

Curated on Posted on by Stefaan Verhulst

Article by Heather Graci: “Contraceptives are available in Sub-Saharan Africa, but maternal deaths caused by unwanted pregnancies are still rampant. Refugee agencies support those forced to flee their homes, but don’t always know where they’ll go—or what they’ll need when they get there. AI-powered tutors provide crucial support to kids struggling in under-resourced schools, but may not treat their students equally. 

These are the sorts of humanitarian challenges that featured at the seventh annual United Nations Behavioural Science Week earlier this month. Each year, the UN Behavioural Science Group brings together researchers and practitioners from inside and outside of the UN to discuss how to use behavioral science for social good. Practitioners are exposed to the latest research that could inform their work; academics glimpse how their ideas play out amid the chaos of the real world. And everyone learns about projects happening beyond their focus area. Experts in healthcare, finance, education, peace and security, and beyond share a common language—and common solutions—in behavioral science. 

This year technology was a central theme. Panelists from organizations like UNICEF and the World Bank joined academic experts from behavioral science, data science, and AI to discuss how thoughtful, behaviorally-informed technologies can bolster global development and aid efforts. 

I’ve curated three sessions from the week that capture the different ways this is happening. Digital assistants that boost the capacity of health care workers or teachers. Predictive models that help aid agencies send the right resources to the right regions. And just as AI can exacerbate bias, it can mitigate it too—as long as we understand how it intersects with different cultures as it’s deployed around the world…(More)”.

Technical Tiers: A New Classification Framework for Global AI Workforce Analysis

Curated on Posted on by Stefaan Verhulst

Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.

Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.

Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.

The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”

Mind the (Language) Gap: Mapping the Challenges of LLM Development in Low-Resource Language Contexts

Curated on Posted on by Stefaan Verhulst

White Paper by the Stanford Institute for Human-Centered AI (HAI), the Asia Foundation and the University of Pretoria: “…maps the LLM development landscape for low-resource languages, highlighting challenges, trade-offs, and strategies to increase investment; prioritize cross-disciplinary, community-driven development; and ensure fair data ownership…

  • Large language model (LLM) development suffers from a digital divide: Most major LLMs underperform for non-English—and especially low-resource—languages; are not attuned to relevant cultural contexts; and are not accessible in parts of the Global South.
  • Low-resource languages (such as Swahili or Burmese) face two crucial limitations: a scarcity of labeled and unlabeled language data and poor quality data that is not sufficiently representative of the languages and their sociocultural contexts.
  • To bridge these gaps, researchers and developers are exploring different technical approaches to developing LLMs that better perform for and represent low-resource languages but come with different trade-offs:
    • Massively multilingual models, developed primarily by large U.S.-based firms, aim to improve performance for more languages by including a wider range of (100-plus) languages in their training datasets.
    • Regional multilingual models, developed by academics, governments, and nonprofits in the Global South, use smaller training datasets made up of 10-20 low-resource languages to better cater to and represent a smaller group of languages and cultures.
    • Monolingual or monocultural models, developed by a variety of public and private actors, are trained on or fine-tuned for a single low-resource language and thus tailored to perform well for that language…(More)”

Artificial Intelligence and Big Data

Curated on Posted on by Stefaan Verhulst

Book edited by Frans L. Leeuw and Michael Bamberger: “…explores how Artificial Intelligence (AI) and Big Data contribute to the evaluation of the rule of law (covering legal arrangements, empirical legal research, law and technology, and international law), and social and economic development programs in both industrialized and developing countries. Issues of ethics and bias in the use of AI are also addressed and indicators of the growth of knowledge in the field are discussed.

Interdisciplinary and international in scope, and bringing together leading academics and practitioners from across the globe, the book explores the applications of AI and big data in Rule of Law and development evaluation, identifies differences in the approaches used in the two fields, and how each could learn from the approaches used in the other, as well as differences in the AI-related issues addressed in industrialized nations compared to those addressed in Africa and Asia.

Artificial Intelligence and Big Data is an essential read for researchers, academics and students working in the fields of Rule of Law and Development, and researchers in institutions working on new applications in AI will all benefit from the book’s practical insights…(More)”.

Designing New Institutions and Renewing Existing Ones – A Playbook

Curated on Posted on by Stefaan Verhulst

UNDP Report: “The world has long depended on public institutions to solve problems and meet needs — from running schools to building roads, taking care of public health to defense. Today, global challenges like climate change, election security, forced migration, and AI-induced unemployment demand new institutional responses, especially in the Global South.

The bad news? Many institutions now struggle with public distrust, being seen as too wasteful
and inefficient, unresponsive and ineffective, and sometimes corrupt and outdated.
The good news? Fresh methods and models inspired by innovations in government, business, and civil
society are now available that can help us rethink institutions — making them more public results
oriented, agile, transparent, and fit for purpose. And ready for the future…(More)”.

Global population data is in crisis – here’s why that matters

Curated on Posted on by Stefaan Verhulst

Article by Andrew J Tatem and Jessica Espey: “Every day, decisions that affect our lives depend on knowing how many people live where. For example, how many vaccines are needed in a community, where polling stations should be placed for elections or who might be in danger as a hurricane approaches. The answers rely on population data.

But counting people is getting harder.

For centuries, census and household surveys have been the backbone of population knowledge. But we’ve just returned from the UN’s statistical commission meetings in New York, where experts reported that something alarming is happening to population data systems globally.

Census response rates are declining in many countries, resulting in large margins of error. The 2020 US census undercounted America’s Latino population by more than three times the rate of the 2010 census. In Paraguay, the latest census revealed a population one-fifth smaller than previously thought.

South Africa’s 2022 census post-enumeration survey revealed a likely undercount of more than 30%. According to the UN Economic Commission for Africa, undercounts and census delays due to COVID-19, conflict or financial limitations have resulted in an estimated one in three Africans not being counted in the 2020 census round.

When people vanish from data, they vanish from policy. When certain groups are systematically undercounted – often minorities, rural communities or poorer people – they become invisible to policymakers. This translates directly into political underrepresentation and inadequate resource allocation…(More)”.

From Insights to Action: Amplifying Positive Deviance within Somali Rangelands

Curated on Posted on by Stefaan Verhulst

Article by Basma Albanna, Andreas Pawelke and Hodan Abdullahi: “In every community, some individuals or groups achieve significantly better outcomes than their peers, despite having similar challenges and resources. Finding these so-called positive deviants and working with them to diffuse their practices is referred to as the Positive Deviance approach. The Data-Powered Positive Deviance (DPPD) method follows the same logic as the Positive Deviance approach but leverages existing, non-traditional data sources, in conjunction with traditional data sources to identify and scale the solutions of positive deviants. The UNDP Somalia Accelerator Lab was part of the first cohort of teams that piloted the application of DPPD trying to tackle the rangeland health problem in the West Golis region. In this blog post we’re reflecting on the process we designed and tested to go from the identification and validation of successful practices to helping other communities adopt them.

Uncovering Rangeland Success

Three years ago we embarked on a journey to identify pastoral communities in Somaliland that demonstrated resilience in the face of adversity. Using a mix of traditional and non-traditional data sources, we wanted to explore and learn from communities that managed to have healthy rangelands despite the severe droughts of 2016 and 2017.

We engaged with government officials from various ministries, experts from the University of Hargeisa, international organizations like the FAO and members of agro-pastoral communities to learn more about rangeland health. We then selected the West Golis as our region of interest with a majority pastoral community and relative ease of access. Employing the Soil-Adjusted Vegetation Index (SAVI) and using geospatial and earth observation data allowed us to identify an initial group of potential positive deviants illustrated as green circles in Figure 1 below.

From Insights to Action: Amplifying Positive Deviance within Somali Rangelands
Figure 1: Measuring the vegetation health within 5 km community buffer zones based on SAVI.

Following the identification of potential positive deviants, we engaged with 18 pastoral communities from the Togdheer, Awdal, and Maroodijeex regions to validate whether the positive deviants we found using earth observation data were indeed doing better than the other communities.

The primary objective of the fieldwork was to uncover the existing practices and strategies that could explain the outperformance of positively-deviant communities compared to other communities. The research team identified a range of strategies, including soil and water conservation techniques, locally-produced pesticides, and reseeding practices as summarized in Figure 2.

From Insights to Action
Figure 2: Strategies and practices that emerged from the fieldwork

Data-Powered Positive Deviance is not just about identifying outperformers and their successful practices. The real value lies in the diffusion, adoption and adaptation of these practices by individuals, groups or communities facing similar challenges. For this to succeed, both the positive deviants and those learning about their practices must take ownership and drive the process. Merely presenting the uncommon but successful practices of positive deviants to others will not work. The secret to success is in empowering the community to take charge, overcome challenges, and leverage their own resources and capabilities to effect change…(More)”.

Privacy guarantees for personal mobility data in humanitarian response

Curated on Posted on by Stefaan Verhulst

Paper by Nitin Kohli,  Emily Aiken & Joshua E. Blumenstock: “Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response…(More)”.