Cross-border data flows in Africa: Continental ambitions and political realities

Curated on Posted on by Stefaan Verhulst

Paper by Melody Musoni, Poorva Karkare and Chloe Teevan: “Africa must prioritise data usage and cross-border data sharing to realise the goals of the African Continental Free Trade Area and to drive innovation and AI development. Accessible and shareable data is essential for the growth and success of the digital economy, enabling innovations and economic opportunities, especially in a rapidly evolving landscape.

African countries, through the African Union (AU), have a common vision of sharing data across borders to boost economic growth. However, the adopted continental digital policies are often inconsistently applied at the national level, where some member states implement restrictive measures like data localisation that limit the free flow of data.

The paper looks at national policies that often prioritise domestic interests and how those conflict with continental goals. This is due to differences in political ideologies, socio-economic conditions, security concerns and economic priorities. This misalignment between national agendas and the broader AU strategy is shaped by each country’s unique context, as seen in the examples of Senegal, Nigeria and Mozambique, which face distinct challenges in implementing the continental vision.

The paper concludes with actionable recommendations for the AU, member states and the partnership with the European Union. It suggests that the AU enhances support for data-sharing initiatives and urges member states to focus on policy alignment, address data deficiencies, build data infrastructure and find new ways to use data. It also highlights how the EU can strengthen its support for Africa’s datasharing goals…(More)”.

Lifecycles, pipelines, and value chains: toward a focus on events in responsible artificial intelligence for health

Curated on Posted on by Stefaan Verhulst

Paper by Joseph Donia et al: “Process-oriented approaches to the responsible development, implementation, and oversight of artificial intelligence (AI) systems have proliferated in recent years. Variously referred to as lifecycles, pipelines, or value chains, these approaches demonstrate a common focus on systematically mapping key activities and normative considerations throughout the development and use of AI systems. At the same time, these approaches risk focusing on proximal activities of development and use at the expense of a focus on the events and value conflicts that shape how key decisions are made in practice. In this article we report on the results of an ‘embedded’ ethics research study focused on SPOTT– a ‘Smart Physiotherapy Tracking Technology’ employing AI and undergoing development and commercialization at an academic health sciences centre. Through interviews and focus groups with the development and commercialization team, patients, and policy and ethics experts, we suggest that a more expansive design and development lifecycle shaped by key events offers a more robust approach to normative analysis of digital health technologies, especially where those technologies’ actual uses are underspecified or in flux. We introduce five of these key events, outlining their implications for responsible design and governance of AI for health, and present a set of critical questions intended for others doing applied ethics and policy work. We briefly conclude with a reflection on the value of this approach for engaging with health AI ecosystems more broadly…(More)”.

A shared destiny for public sector data

Curated on Posted on by Stefaan Verhulst

Blog post by Shona Nicol: “As a data professional, it can sometime feel hard to get others interested in data. Perhaps like many in this profession, I can often express the importance and value of data for good in an overly technical way. However when our biggest challenges in Scotland include eradicating child poverty, growing the economy and tackling the climate emergency, I would argue that we should all take an interest in data because it’s going to be foundational in helping us solve these problems.

Data is already intrinsic to shaping our society and how services are delivered. And public sector data is a vital component in making sure that services for the people of Scotland are being delivered efficiently and effectively. Despite an ever growing awareness of the transformative power of data to improve the design and delivery of services, feedback from public sector staff shows that they can face difficulties when trying to influence colleagues and senior leaders around the need to invest in data.

A vision gap

In the Scottish Government’s data maturity programme and more widely, we regularly hear about the challenges data professionals encounter when trying to enact change. This community tell us that a long-term vision for public sector data for Scotland could help them by providing the context for what they are trying to achieve locally.

Earlier this year we started to scope how we might do this. We recognised that organisations are already working to deliver local and national strategies and policies that relate to data, so any vision had to be able to sit alongside those, be meaningful in different settings, agnostic of technology and relevant to any public sector organisation. We wanted to offer opportunities for alignment, not enforce an instruction manual…(More)”.

Understanding local government responsible AI strategy: An international municipal policy document analysis

Curated on Posted on by Stefaan Verhulst

Paper by Anne David et al: “The burgeoning capabilities of artificial intelligence (AI) have prompted numerous local governments worldwide to consider its integration into their operations. Nevertheless, instances of notable AI failures have heightened ethical concerns, emphasising the imperative for local governments to approach the adoption of AI technologies in a responsible manner. While local government AI guidelines endeavour to incorporate characteristics of responsible innovation and technology (RIT), it remains essential to assess the extent to which these characteristics have been integrated into policy guidelines to facilitate more effective AI governance in the future. This study closely examines local government policy documents (n = 26) through the lens of RIT, employing directed content analysis with thematic data analysis software. The results reveal that: (a) Not all RIT characteristics have been given equal consideration in these policy documents; (b) Participatory and deliberate considerations were the most frequently mentioned responsible AI characteristics in policy documents; (c) Adaptable, explainable, sustainable, and accountable considerations were the least present responsible AI characteristics in policy documents; (d) Many of the considerations overlapped with each other as local governments were at the early stages of identifying them. Furthermore, the paper summarised strategies aimed at assisting local authorities in identifying their strengths and weaknesses in responsible AI characteristics, thereby facilitating their transformation into governing entities with responsible AI practices. The study informs local government policymakers, practitioners, and researchers on the critical aspects of responsible AI policymaking…(More)” See also: AI Localism

AI helped Uncle Sam catch $1 billion of fraud in one year. And it’s just getting started

Curated on Posted on by Stefaan Verhulst

Article by Matt Egan: “The federal government’s bet on using artificial intelligence to fight financial crime appears to be paying off.

Machine learning AI helped the US Treasury Department to sift through massive amounts of data and recover $1 billion worth of check fraud in fiscal 2024 alone, according to new estimates shared first with CNN. That’s nearly triple what the Treasury recovered in the prior fiscal year.

“It’s really been transformative,” Renata Miskell, a top Treasury official, told CNN in a phone interview.

“Leveraging data has upped our game in fraud detection and prevention,” Miskell said.

The Treasury Department credited AI with helping officials prevent and recover more than $4 billion worth of fraud overall in fiscal 2024, a six-fold spike from the year before.

US officials quietly started using AI to detect financial crime in late 2022, taking a page out of what many banks and credit card companies already do to stop bad guys.

The goal is to protect taxpayer money against fraud, which spiked during the Covid-19 pandemic as the federal government scrambled to disburse emergency aid to consumers and businesses.

To be sure, Treasury is not using generative AI, the kind that has captivated users of OpenAI’s ChatGPT and Google’s Gemini by generating images, crafting song lyrics and answering complex questions (even though it still sometimes struggles with simple queries)…(More)”.

Statistical Significance—and Why It Matters for Parenting

Curated on Posted on by Stefaan Verhulst

Blog by Emily Oster: “…When we say an effect is “statistically significant at the 5% level,” what this means is that there is less than a 5% chance that we’d see an effect of this size if the true effect were zero. (The “5% level” is a common cutoff, but things can be significant at the 1% or 10% level also.) 

The natural follow-up question is: Why would any effect we see occur by chance? The answer lies in the fact that data is “noisy”: it comes with error. To see this a bit more, we can think about what would happen if we studied a setting where we know our true effect is zero. 

My fake study 

Imagine the following (fake) study. Participants are randomly assigned to eat a package of either blue or green M&Ms, and then they flip a (fair) coin and you see if it is heads. Your analysis will compare the number of heads that people flip after eating blue versus green M&Ms and report whether this is “statistically significant at the 5% level.”…(More)”.

External Researcher Access to Closed Foundation Models

Curated on Posted on by Stefaan Verhulst

Report by Esme Harrington and Dr. Mathias Vermeulen: “…addresses a pressing issue: independent researchers need better conditions for accessing and studying the AI models that big companies have developed. Foundation models — the core technology behind many AI applications — are controlled mainly by a few major players who decide who can study or use them.

What’s the problem with access?

  • Limited access: Companies like OpenAI, Google and others are the gatekeepers. They often restrict access to researchers whose work aligns with their priorities, which means independent, public-interest research can be left out in the cold.
  • High-end costs: Even when access is granted, it often comes with a hefty price tag that smaller or less-funded teams can’t afford.
  • Lack of transparency: These companies don’t always share how their models are updated or moderated, making it nearly impossible for researchers to replicate studies or fully understand the technology.
  • Legal risks: When researchers try to scrutinize these models, they sometimes face legal threats if their work uncovers flaws or vulnerabilities in the AI systems.

The research suggests that companies need to offer more affordable and transparent access to improve AI research. Additionally, governments should provide legal protections for researchers, especially when they are acting in the public interest by investigating potential risks…(More)”.

The Number

Curated on Posted on by Stefaan Verhulst

Article by John Lanchester: “…The other pieces published in this series have human protagonists. This one doesn’t: The main character of this piece is not a person but a number. Like all the facts and numbers cited above, it comes from the federal government. It’s a very important number, which has for a century described economic reality, shaped political debate and determined the fate of presidents: the consumer price index.

The CPI is crucial for multiple reasons, and one of them is not because of what it is but what it represents. The gathering of data exemplifies our ambition for a stable, coherent society. The United States is an Enlightenment project based on the supremacy of reason; on the idea that things can be empirically tested; that there are self-evident truths; that liberty, progress and constitutional government walk arm in arm and together form the recipe for the ideal state. Statistics — numbers created by the state to help it understand itself and ultimately to govern itself — are not some side effect of that project but a central part of what government is and does…(More)”.

Key lesson of this year’s Nobel Prize: The importance of unlocking data responsibly to advance science and improve people’s lives

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst, Anna Colom, and Marta Poblet: “This year’s Nobel Prize for Chemistry owes a lot to available, standardised, high quality data that can be reused to improve people’s lives. The winners, Prof David Baker from the University of Washington, and Demis Hassabis and John M. Jumper from Google DeepMind, were awarded respectively for the development and prediction of new proteins that can have important medical applications. These developments build on AI models that can predict protein structures in unprecedented ways. However, key to these models and their potential to unlock health discoveries is an open curated dataset with high quality and standardised data, something still rare despite the pace and scale of AI-driven development.

We live in a paradoxical time of both data abundance and data scarcity: a lot of data is being created and stored, but it tends to be inaccessible due to private interests and weak regulations. The challenge, then, is to prevent the misuse of data whilst avoiding its missed use.

The reuse of data remains limited in Europe, but a new set of regulations seeks to increase the possibilities of responsible data reuse. When the European Commission made the case for its European Data Strategy in 2020, it envisaged the European Union “a role model for a society empowered by data to make better decisions — in business and the public sector,” and acknowledged the need to improve “governance structures for handling data and to increase its pools of quality data available for use and reuse”…(More)”.

How Artificial Intelligence Can Support Peace

Curated on Posted on by Stefaan Verhulst

Essay by Adam Zable, Marine Ragnet, Roshni Singh, Hannah Chafetz, Andrew J. Zahuranec, and Stefaan G. Verhulst: “In what follows we provide a series of case studies of how AI can be used to promote peace, leveraging what we learned at the Kluz Prize for PeaceTech and NYU Prep and Becera events. These case studies and applications of AI are limited to what was included in these initiatives and are not fully comprehensive. With these examples of the role of technology before, during, and after a conflict, we hope to broaden the discussion around the potential positive uses of AI in the context of today’s global challenges.

Ai for Peace Blog GraphicThe table above summarizes the how AI may be harnessed throughout the conflict cycle and the supporting examples from the Kluz Prize for PeaceTech and NYU PREP and Becera events

(1) The Use of AI Before a Conflict

AI can support conflict prevention by predicting emerging tensions and supporting mediation efforts. In recent years, AI-driven early warning systems have been used to identify patterns that precede violence, allowing for timely interventions. 

For instance, The Violence & Impacts Early-Warning System (VIEWS), developed by a research consortium at Uppsala University in Sweden and the Peace Research Institute Oslo (PRIO) in Norway, employs AI and machine learning algorithms to analyze large datasets, including conflict history, political events, and socio-economic indicators—supporting negative peace and peacebuilding efforts. These algorithms are trained to recognize patterns that precede violent conflict, using both supervised and unsupervised learning methods to make predictions about the likelihood and severity of conflicts up to three years in advance. The system also uses predictive analytics to identify potential hotspots, where specific factors—such as spikes in political unrest or economic instability—suggest a higher risk of conflict…(More)”.