Energy poverty remains a significant challenge in Sub-Saharan Africa (SSA), where approximately 600 million people lack proper access to electricity. This paper examines the region’s current state of energy poverty, highlighting its socio-economic impacts and the barriers to achieving Sustainable Development Goal 7 (SDG7), which aims for affordable, reliable, sustainable, and modern energy for all by 2030. Despite the region’s rich renewable energy potential, inadequate infrastructure, economic constraints, and governance issues continue to impede progress. This work employs a doctrinal research methodology, focusing on the critical analysis of existing legal and policy frameworks relevant to energy poverty and the integration of AI in energy management. This paper presents an overview of energy poverty in SSA, underpinned by current statistics and trends. It then examines the dual role of artificial intelligence (AI) and how it impacts this area: while AI technologies, through its data centre s, for example, significantly increase energy consumption, AI also offers innovative solutions for energy management, efficiency, and the integration of renewable energy sources. This paper critically analyzes these dynamics using Marxist and Third World Approaches to International Law (TWAIL) frameworks to understand the broader socio-economic inequalities and global power dynamics at play. Major findings indicate that current policy frameworks are inadequate in addressing the unique challenges of energy poverty and the growing role of AI in the energy sector. The paper reviews existing policy and regulatory frameworks, identifying gaps and proposing actionable recommendations for integrating AI into policies to address energy poverty. It concludes with actionable policy recommendations to achieve a just and inclusive energy transition, contributing to the broader discourse on sustainable development and technological equity.
Author: Aula Blog Editor
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Easy to read, easier to write: the politics of AI in consultancy trade research
AI systems have been rapidly implemented in all sectors, of all sizes and in every country. In this article, we conduct a bibliometric review of references in recent consultancy reports on AI use in business, policymaking, and strategic management. The uptake of these reports is high. We find three positive factors: focus on client-facing solutions, speed of production, and ease of access. We find that the evidentiary quality of reports is often unsatisfactory because of references-clubbing with other consultancy reports, references to surveys without transparency, or poor or missing references. To optimize the utility of consultancy reports for decision-makers and their pertinence for policy, we present recommendations for the quality assessment of consultancy reporting on AI’s use in organizations. We discuss how to improve general knowledge of AI use in business and policymaking, through effective collaborations between consultants and management scientists. In addition to being of interest to managers and consultants, this work may also be of interest to media, political scientists, and business-school communities.
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Options and Motivations for International AI Benefit Sharing
Advanced AI systems could generate substantial economic and other societal benefits, but these benefits may not be widely shared by default. For a range of reasons, a number of prominent actors and institutions have called for efforts to expand access to AI’s benefits. In this report, we define the concept of international AI benefit sharing (“benefit sharing”) as efforts to support and accelerate international access to AI’s economic or broader societal benefits. Calls for benefit sharing typically invoke at least one of three motivations: 1) supporting inclusive economic growth and sustainable development, 2) fostering technological self-determination in low- and middle-income countries, and 3) advancing geopolitical objectives, including strengthening international partnerships on AI governance. Notably, as a subset of the third motive, some powerful actors – like the US government – may support benefit sharing as a tool to further their economic and national security interests. Benefit sharing could be implemented by (1) sharing AI resources (e.g., computing power or data), (2) expanding access to AI systems, or (3) transferring a portion of the financial proceeds from AI commercialisation or AI-driven economic growth. Depending on the objective that benefit sharing is intended to achieve, each of these approaches offers distinct opportunities and implementation challenges. These challenges include the potential for some benefit-sharing options to raise security concerns and increase certain global risks. Actors interested in benefit sharing may consider implementing low-risk forms of benefit sharing immediately, while launching cooperative international discussions to develop more comprehensive, mutually-beneficial initiatives.
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UAE Abaya Fashion: From Cover to Prestige (and Social Liberalization)
This paper examines the power of the UAE abaya. Moreover, it is concerned with the exploitation of luxury in the pursuit of social status and the attainment of greater freedom within an authoritarian context. As will be argued, the abaya has transitioned from serving the state in the process of identity formation to becoming a non-state actor capable of challenging dominant strictures and providing for policy alternatives. However, while the new or revamped abaya has contributed to self-actualization and made taboo topics more visible, it is also important to note that some Emiratis or minority groups may end up being excluded from this largely luxury-driven process. For the leadership, this could create an unenviable situation, particularly when considering the potential rift between the promises outlined in the state vision and the prerequisites needed for its implementation. With this in mind, the present analysis is also intended to assist policymakers working on tolerance and social cohesion, as well as those striving to position the UAE as a major point of reference in global affairs.
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AI between Democracy and Authoritarianism
The perversion of democracy has advanced somuch that it has become a caricature of itself.Many of the features associated with authoritarian regimes—nepotism and corruption, major inequalities and abuses of human rights, as well as the provision of a havenfor war profiteersand business criminals—are also associated with officially-labeled democratic environments. Even though democracy is superior to other regimes in terms of “self-correction” as prompted by the principles of “evaluation, political competition, and freedom of expression,” democracies have regularly trapped themselves in policies that expose bigotryanddouble standards. For example, while the West (the US, in particular) supported non-democratic regimes in its fight against Communism during the Cold War, the EU’s involvement in the Arab Spring has opened questions about whether it has eventually assisted authoritarian instead of democratic rule. Therefore, it seemsperfectly fine that the controversial FIFA awardsthe hosting of the World Cup to both democratic and authoritarian regimes, or that theNorwegian Nobel Committee repeatedly awards the Peace Prize to individuals or supranational entities with dubious performance vis-à-vis democratic postulates.
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Data Journalism Appropriation in African Newsrooms: A Comparative Study of Botswana and Namibia
Data journalism has received relatively limited academic attention in Southern Africa, with even less focus on smaller countries such as Botswana and Namibia. This article seeks to address this gap by exploring how selected newsrooms in these countries have engaged with data journalism, the ways it has enhanced their daily news reporting, and its impact on newsgathering and production routines. The study reveals varied patterns in the adoption of technology for data journalism across the two contexts. While certain skills remain underdeveloped, efforts to train journalists in data journalism have been evident. These findings support the argument that in emerging economies, the uneven adoption of data journalism technologies is influenced by exposure to these tools and practices.
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IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding
Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures. IndicMMLU-Pro is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across Indic languages, building upon the MMLU Pro (Massive Multitask Language Understanding) framework. Covering major languages such as Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the Indian subcontinent. This benchmark encompasses a wide range of tasks in language comprehension, reasoning, and generation, meticulously crafted to capture the intricacies of Indian languages. IndicMMLU-Pro provides a standardized evaluation framework to push the research boundaries in Indic language AI, facilitating the development of more accurate, efficient, and culturally sensitive models. This paper outlines the benchmarks’ design principles, task taxonomy, and data collection methodology, and presents baseline results from state-of-the-art multilingual models.
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Mediatized discourses on Europeanization in Spain
Political and media polarization has had a detrimental impact on democratic principles and democratic processes on a
global scale. In Europe, such polarization has eroded the trust in national and European institutions and has challenged the
basic values that stand at the heart of the European integration project. The aim of this study is to analyze Spanish media discourses on Europeanization, with an attempt to identify key areas in which polarizing narratives related to Europeanization
are more prevalent. To conduct our study, six national media outlets were selected based on four criteria: media format,
ownership, ideology, and consumption. A final sample of 540 news items collected between July 2021 to March 2022 was
selected for analysis. Using a qualitative methodological approach, the study was carried out in two stages. In the first
phase, we conducted a content analysis to identify the main topics discussed in relation to the European Union and the
actors represented in them. This led to the identification of polarizing narratives and discourses emerging in the context
of the discussed topics. In the second phase, we used critical discourse analysis to analyze polarizing discourses. -
Potential and perils of large language models as judges of unstructured textual data
Rapid advancements in large language models have unlocked remarkable capabilities when it comes to processing and summarizing unstructured text data. This has implications for the analysis of rich, open-ended datasets, such as survey responses, where LLMs hold the promise of efficiently distilling key themes and sentiments. However, as organizations increasingly turn to these powerful AI systems to make sense of textual feedback, a critical question arises, can we trust LLMs to accurately represent the perspectives contained within these text based datasets? While LLMs excel at generating human-like summaries, there is a risk that their outputs may inadvertently diverge from the true substance of the original responses. Discrepancies between the LLM-generated outputs and the actual themes present in the data could lead to flawed decision-making, with far-reaching consequences for organizations. This research investigates the effectiveness of LLM-as-judge models to evaluate the thematic alignment of summaries generated by other LLMs. We utilized an Anthropic Claude model to generate thematic summaries from open-ended survey responses, with Amazon’s Titan Express, Nova Pro, and Meta’s Llama serving as judges. This LLM-as-judge approach was compared to human evaluations using Cohen’s kappa, Spearman’s rho, and Krippendorff’s alpha, validating a scalable alternative to traditional human centric evaluation methods. Our findings reveal that while LLM-as-judge offer a scalable solution comparable to human raters, humans may still excel at detecting subtle, context-specific nuances. Our research contributes to the growing body of knowledge on AI assisted text analysis. Further, we provide recommendations for future research, emphasizing the need for careful consideration when generalizing LLM-as-judge models across various contexts and use cases.
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Power Preservation, No Matter the Means: Populism and Conspiracy Theory as Instruments of Political Consolidation in Serbia
This article examines the Serbian political leadership—the president and government alike—by addressing the dominant political figures’ narratives. We communicate with the theoretical aspects in the study of populism and conspiracy theories as this nexus enables us to examine the specific nature of the domestic politics in Serbia. In our view, the ruling elite complements its populist discourse with conspiracy theory to ensure its survival in power, by regularly generating fear about the threat posed to Serbian statehood and lack of apprehension for Belgrade’s geopolitical preferences and exploration of foreign policy alternatives. Our analysis fills a major gap in the literature, since there has been only sporadic research on this topic and none of it has focused on the merger of populism and conspiracy theory. The findings we have reached—largely those of the elite’s self-victimization narratives and their dissemination of anti-Western sentiments—provide for a fresh contribution to the debate concerning the power struggle and the state of democracy in Serbia, especially given the fact that the key political stakeholders draw heavily on pro-regime media outlets to readily disseminate their self-serving accounts.
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Dataset: Consultancy reports on AI in Business (Full Systematic Review)
This dataset is available for collaborations. Please contact our research Director, Dr. Branislav Radeljic, Ph.D., for more information.
Used in: Easy to read, easier to write: the politics of AI in consultancy trade research
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Dataset: Survey of leaders on their institutional purview over AI (anonymized)
This dataset is under construction and not yet available for collaborations. We expect to publish a dataset report on this blog in November, 2025. Please contact our Research Director, Dr. Branislav Radeljic, Ph.D., for more information.