In recent years, media organizations globally have increasingly benefited from financial support from digital platforms. In 2018, Google launched the Google News Initiative (GNI) Innovation Challenge aimed at bolstering journalism by encouraging innovation in media organizations. This study, conducted through 36 in-depth interviews with GNI beneficiaries in Africa, Latin America, and the Middle East, reveals that despite its narrative of enhancing technological innovation for the media’s future, this scheme inadvertently fosters dependence and extends the philanthrocapitalism concept to the media industry on a global scale. Employing a theory-building approach, our research underscores the emergence of a new form of ‘philanthrocapitalism’ that prompts critical questions about the dependency of media organizations on big tech and the motives of these tech giants in their evolving relationship with such institutions. We also demonstrate that the GNI Innovative Challenge, while ostensibly promoting sustainable business models through technological innovation, poses challenges for organizations striving to sustain and develop these projects. The proposed path to sustainability by the GNI is found to be indirect and difficult for organizations to navigate, hindering their adoption of new technologies. Additionally, the study highlights the creation of a dependency syndrome among news organizations, driven by the perception that embracing GNI initiatives is crucial for survival in the digital age. Ultimately, the research contributes valuable insights to the understanding of these issues, aiming to raise awareness among relevant stakeholders and conceptualize philanthrocapitalism through a new lens.
Category: Type: Research
In this category:
1. Peer-Reviewed:
Research Papers
Chapters
Conference Proceedings
2. Pre-Prints. Pre-Prints are standard in some fields. They are not always peer reviewed.
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Canary in the Mine: An LLM Augmented Survey of Disciplinary Complaints to the Ordre des ingénieurs du Québec (OIQ) (Peer Reviewed)
This study investigates disciplinary incidents involving engineers in Quebec, shedding light on critical gaps in engineering education. Through a comprehensive review of the disciplinary register of the Ordre des ingénieurs du Québec (OIQ)’s disciplinary register for 2010 to 2024, researchers from engineering education and human resources management in technological development laboratories conducted a thematic analysis of reported incidents to identify patterns, trends, and areas for improvement. The analysis aims to uncover the most common types of disciplinary incidents, underlying causes, and implications for the field in how engineering education addresses (or fails to address) these issues. Our findings identify recurring themes, analyze root causes, and offer recommendations for engineering educators and students to mitigate similar incidents. This research has implications for informing curriculum development, professional development, and performance evaluation, ultimately fostering a culture of professionalism and ethical responsibility in engineering. By providing empirical evidence of disciplinary incidents and their causes, this study contributes to evidence-based practices for engineering education and professional development, enhancing the engineering education community’s understanding of professionalism and ethics.
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What We Do Not Know: GPT Use in Business and Management
This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.
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A labeled clinical-MRI dataset of Nigerian brains
There is currently a paucity of neuroimaging data from the African continent, limiting the diversity of data from a significant proportion of the global population. This in turn diminishes global health research and innovation. To address this issue, we present and describe the first Magnetic Resonance Imaging (MRI) dataset from individuals in the African nation of Nigeria. This dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality, with 35 images from healthy control subjects, 31 images from individuals diagnosed with age-related dementia, and 22 from individuals with Parkinson’s Disease. Given the potential for Africa to contribute to the global neuroscience community, this unique MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent.
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Community energy justice: A review of origins, convergence, and a research agenda
The transition to zero‑carbon sustainable energy systems is critical and must take an equity-oriented approach to avoid exacerbating societal injustices. We explore the concept of “community” and its potential as a viable and effective tool for studying, understanding, and fostering justice and equity in energy transitions. This paper outlines community energy justice as an area of scholarship emerging through convergence around three key concepts: community, energy transition, and justice. Using a narrative literature review approach, we unpack the origins of community energy justice research, rooted in two scholarship pillars of energy justice and community energy. We outline four driving forces and two key approaches leading to convergence between both areas of scholarship. Encompassing energy transition initiatives that incorporate both justice and community themes, we find that the overarching objective …
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‘Mind the gap’: artificial intelligence and journalism training in Southern African journalism schools
This article examines journalism schools (J-schools) responses to the Artificial Intelligence (AI) ‘disruption’. It critically provides an exploratory examination of how J-Schools in Southern Africa are responding to the AI wave in their journalism curriculums. We answer the question: How are Southern African J-Schools responding to AI in their curriculums? Using a disruptive innovation theoretical lens and through documentary review of university teaching initiatives and accredited journalism curriculums, augmented by in-depth interviews, we demonstrate that AI has opened up new horizons for journalism training in multi-dimensional ways. However, this has brought challenges, including covert forms of resistance to AI integration by some Journalism educators. Furthermore, resource constraints and the obduracy of J-schools’ curriculums also contribute to the slow introduction of AI in J-schools.
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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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Africa’s Energy Poverty in An Artificial Intelligence (AI) World: Struggle for Sustainable Development Goal 7
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.
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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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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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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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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.