Can a Big Tech company be sued in Kenya for alleged abuses at an outsourcing company working on its behalf? That's the question at the heart of two lawsuits that are attempting to set a new precedent in Kenya, which is the prime destination for tech companies looking to farm out digital work to the African continent. The two-year legal battle stems from allegations of human rights violations at an outsourced Meta content moderation facility in Nairobi, where employees hired by a contractor were paid as little as 1.50 per hour to view traumatic content, such as videos of rapes, murders, and war crimes. The suits claim that despite the workers being contracted by an outsourcing company, called Sama, Meta essentially supervised and set the terms for the work, and designed and managed the software required for the task. Both companies deny wrongdoing and Meta has challenged the Kenyan courts' jurisdiction to hear the cases.

Structural transformation, the shift from agrarian economies to more diversified industrial and service-based systems, is a key driver of economic development. However, in low- and middle-income countries (LMICs), data scarcity and unreliability hinder accurate assessments of this process. This paper presents a novel statistical framework designed to address these challenges by integrating Bayesian hierarchical modeling, machine learning-based data imputation, and factor analysis. The framework is specifically tailored for conditions of data sparsity and is capable of providing robust insights into sectoral productivity and employment shifts across diverse economies. By utilizing Bayesian models, uncertainties in data are effectively managed, while machine learning techniques impute missing data points, ensuring the integrity of the analysis. Factor analysis reduces the dimensionality of complex datasets, distilling them into core economic structures. The proposed framework has been validated through extensive simulations, demonstrating its ability to predict structural changes even when up to 60\% of data is missing. This approach offers policymakers and researchers a valuable tool for making informed decisions in environments where data quality is limited, contributing to the broader understanding of economic development in LMICs.

The Economic and Product Complexity Indices, introduced as an attempt to measure these capabilities from a country's basket of exported products, have become popular to study economic development, the geography of innovation, and industrial policies. Despite this reception, the interpretation of these indicators proved difficult. Although the original Method of Reflections suggested a direct interconnection between country and product metrics, it has been proved that the Economic and Product Complexity Indices result from a spectral clustering algorithm that separately groups similar countries or similar products, respectively. This recent approach to economic and product complexity conflicts with the original one and treats separately countries and products. However, building on previous interpretations of the indices and the recent evolution in spectral clustering, we show that these indices simultaneously identify two co-clusters of similar countries and products. This viewpoint reconciles the spectral clustering interpretation of the indices with the original Method of Reflections interpretation. By proving the often neglected intimate relationship between country and product complexity, this approach emphasizes the role of a selected set of products in determining economic development while extending the range of applications of these indicators in economics.

The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessibility and economic development. Although publicly available data like OpenStreetMap is available to monitor road status, data completeness in impoverished areas is limited. Meanwhile, the development of deep learning techniques and satellite imagery shows excellent potential for earth monitoring. To tackle the challenge of road network assessment in impoverished areas, we develop a systematic road extraction framework combining an encoder-decoder architecture and morphological operations on satellite imagery, offering an integrated workflow for interdisciplinary researchers. Extensive experiments of road network extraction on real-world data in impoverished regions achieve a 42.7% enhancement in the F1-score over the baseline methods and reconstruct about 80% of the actual roads. We also propose a comprehensive road network dataset covering approximately 794,178 km2 area and 17.048 million people in 382 impoverished counties in China. The generated dataset is further utilized to conduct socioeconomic analysis in impoverished counties, showing that road network construction positively impacts regional economic development. The technical appendix, code, and generated dataset can be found at https://github.com/tsinghua-fib-lab/Road_network_extraction_impoverished_counties.

Abstract: By integrating survival analysis, machine learning algorithms, and economic interpretation, this research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a period of 120 months (2013-2022). A comparative investigation reveals that DeepSurv is proficient at capturing non-linear interactions, although standard models exhibit comparable performance under certain circumstances. The weight matrix evaluates the economic ramifications of vulnerabilities, risks, and capacities. In order to meet the GDPpc objective, the findings emphasize the need of a balanced approach to risk-taking, strategic vulnerability reduction, and investment in governmental capacities and social cohesiveness. Policy guidelines promote individualized approaches that take into account the complex dynamics at play while making decisions. JEL: 04, C8, C5, O1 1. Introduction In contemporary economic research, the exploration of temporal dynamics in a nation's journey to achieve a specific level of GDP per capita gains paramount importance. This empirical investigation, conducted across 33 American countries, adopts a nuanced approach by incorporating a comprehensive dataset that includes countries with right-censored data (9 countries) and those reaching a 5% increase in GDP per capita at purchasing power parity (PIBpcPPP) within 120 months (24 countries). In addressing the central query, this research aims to unravel the intricate relationship of variables and risks influencing the time required for a country to achieve the specified 5% increase in GDP per capita. Leveraging advanced statistical techniques, particularly survival analysis, the study incorporates key variables such as Vul_Inherent, Vul_Fragility_Democracy, and Vul_Human Rights, offering a robust understanding of multifaceted vulnerabilities. This academic pursuit emphasizes rigorous methodologies, empirical analyses, and data-driven insights.

AI has extraordinary potential and developing countries must move forward quickly in this field to leverage their technological prowess, productivity, and competitiveness. Certainly, investing in R&D, developing capacities, and retaining AI talent is much easier said than done. Besides adopting a national AI strategy, if there is none, developing countries could put into practice a roadmap with clearly defined priorities and projects that bolster the economy. They can also build partnerships and reach out to other countries and organizations that are willing to cooperate in frontier technologies. A niche strategy might help to leapfrog in a few select sectors, as in the case of some small states that have become active players in the digital sphere. Interestingly enough, Kenya became last August the first African country to teach coding as a subject in schools. As stated in the UNCTAD 2021 Digital Economy report, developing countries risk becoming mere providers of data, while having to pay for digital intelligence produced with their data. Current international regulatory frameworks tend to be either too narrow in scope or too limited geographically, failing to enable cross-border data flows with an equitable sharing of economic gains. In a nutshell, developing countries need to find the optimal balance between promoting domestic economic development, protecting public policy interests, and integrating into the global digital ecosystem.

This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.

Sber's international online conference "Artificial Intelligence Journey" culminated in a discussion titled "AI Technology to Address Social Issues", in which the President of the Russian Federation, Vladimir Putin, participated. The discussion was moderated by Sber CEO and Chairman of the Sberbank Executive Board, Herman Gref. The session was attended by the winners of the AI International Junior Contest, organized by Sber in partnership with the Artificial Intelligence Alliance. This year's conference hit all-time record, with 52,000 participants. Over 800 people presented their solutions to AI challenges, including innovative approaches to Strong AI and Artificial General Intelligence.

Hallam is an Associate Professor in the History Programme and in the School of Biological Sciences at Nanyang Technological University in Singapore. Daniel is an external PhD candidate at eLaw -- Center for Law and Digital Technologies, Leiden University, the Netherlands. This essay is part of our ongoing "AI Lexicon" project, a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. Much of the history, meaning, and imagination of AI is discussed in relation to the West, often against a backdrop of cybernetics, "AI winters," and Terminator androids. These narratives inform how we understand the risks and "social good" of AI.

Progress in artificial intelligence and related forms of automation technologies threatens to reverse the gains that developing countries and emerging markets have experienced from integrating into the world economy over the past half century, aggravating poverty and inequality. The new technologies have the tendency to be labor-saving, resource-saving, and to give rise to winner-takes-all dynamics that advantage developed countries. We analyze the economic forces behind these developments and describe economic policies that would mitigate the adverse effects on developing and emerging economies while leveraging the potential gains from technological advances. We also describe reforms to our global system of economic governance that would share the benefits of AI more widely with developing countries.