Join Victoria Kuketz for an intercultural Skills Lab Panel “Building Bridges”, for the Culture and Cohesion Summit.
Category: 3/ Type
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Advancements in Modern Recommender Systems: Industrial Applications in Social Media, E-commerce, Entertainment, and Beyond
In the current digital era, the proliferation of online content has overwhelmed users with vast amounts of information, necessitating effective filtering mechanisms. Recommender systems have become indispensable in addressing this challenge, tailoring content to individual preferences and significantly enhancing user experience. This paper delves into the latest advancements in recommender systems, analyzing 115 research papers and 10 articles, and dissecting their application across various domains such as e-commerce, entertainment, and social media. We categorize these systems into content-based, collaborative, and hybrid approaches, scrutinizing their methodologies and performance. Despite their transformative impact, recommender systems grapple with persistent issues like scalability, cold-start problems, and data sparsity. Our comprehensive review not only maps the current landscape of recommender system research but also identifies critical gaps and future directions. By offering a detailed analysis of datasets, simulation platforms, and evaluation metrics, we provide a robust foundation for developing next-generation recommender systems poised to deliver more accurate, efficient, and personalized user experiences, inspiring innovative solutions to drive forward the evolution of recommender technology.
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Unravelling socio-technological barriers to AI integration: A qualitative study of Southern African newsrooms
This study explores the socio-technological barriers to the adoption of artificial intelligence (AI)-powered solutions in three countries of the global south – South Africa, Lesotho, Eswatini, Botswana and Zimbabwe. Through 20 in-depth interviews with key stakeholders, it examines the distribution and circulation of AI technologies within selected newsrooms. Furthermore, the article explores socio-technological obstacles to the integration of AI among journalists. Lastly, it examines the consequences of these socio-technological obstacles to journalism. The article specifically seeks to answer three questions: How are AI technologies integrated in southern African newsrooms? What are the socio-technological barriers attendant to the use of AI in selected news organisations of sub-Saharan Africa? What are the implications of these socio-technological barriers to the process of news production in these newsrooms?
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The Climate Imperative: How AI Can Transform Africa’s Future
Africa contributes minimally to global greenhouse gas emissions but bears a disproportionate burden of climate change impacts. This article explores how artificial intelligence (AI) can bolster conservation and sustainability efforts across the continent. While challenges such as technological import reliance and digital divides persist, AI offers transformative potential by enhancing early prediction, disaster preparedness, and environmental management. Examples like Rwanda’s Wastezon, Ghana’s Okuafo Foundation, and Kenya’s Kuzi illustrate successful AI-driven initiatives. The article proposes adapting a public health prevention model-primary, secondary, and tertiary prevention-to structure AI-based environmental interventions. This approach would enable early detection of climate risks, timely mitigation efforts, and rehabilitation of damaged ecosystems. The authors also caution about AI’s environmental costs, including energy-intensive operations and resource extraction, advocating for ethical and Africa-centered AI solutions. Overall, the article argues that innovative, community-driven, and preventive uses of AI are essential for building climate resilience in Africa.
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Reconciling methodological paradigms: Employing large language models as novice qualitative research assistants in talent management research
Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent
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Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa
This article proposes five ideas that the design of data governance policies for the trustworthy use of artificial intelligence (AI) in Africa should consider. The first is for African states to assess their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance, which involves data processing practices that protect the security of personal data and the privacy of data subjects; ensure that personal data are processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity, and Personal Data Protection. The fourth is for states to be critical about the extent to which AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensure a transparent procurement process for AI systems from abroad where no local options exist.
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Path to Personalization: A Systematic Review of GenAI in Engineering Education
This systematic review paper provides a comprehensive synthesis across 162 articles on Generative Artificial Intelligence (GenAI) in engineering education (EE), making two specific contributions to advance research in the space. First, we develop a taxonomy that categorizes the current research landscape, identifying key areas such as Coding or Writing Assistance, Design Methodology, and Personalization. Second, we highlight significant gaps and opportunities, such as lack of customer-centricity and need for increased transparency in future research, paving the way for increased personalization in GenAI-augmented engineering education. There are indications of widening lines of enquiry, for example into human-AI collaborations and multidisciplinary learning. We conclude that there are opportunities to enrich engineering epistemology and
competencies with the use of GenAI tools for educators and students, as well as a need for further research into best and novel practices. Our discussion serves as a roadmap for researchers and educators, guiding the development of GenAI applications that will continue to transform the engineering education landscape, in classrooms and the workforce. -
Collision Toronto: Booth
Emmanuel Taiwo and Tammy Mackenzie represented the Aula Fellowship in the social good start-up stage. Thank you, Collision Toronto, for this excellent experience with old and new friends, working for good AI together in excellent company. Thanks also to Aula Fellow Rubaina Khan, Victoria Kuketz, and Marisa Eleuterio, for technical support throughout!
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Collision Toronto: Presentation
Presented the Aula Fellowship from the Non-Profits Stage. Thank you, Victoria!
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Digitization and Political Participation in the MENA Region: Egypt, Kuwait, and Tunisia
The article highlights the link between digitization and political participation in three Middle Eastern countries: Egypt, Kuwait, and Tunisia. The role of the Internet and social media in political engagement is thoroughly discussed from a historical-comparative perspective. Using micro and macro level data, the study analyzes the usage of new online technologies and online political participation.
The findings provide valuable insights for understanding the intricate nature of online political participation and the paradox between digital engagement and traditional political involvement. Despite the expansion of digital media, traditional political interest and participation has decreased. Egypt and Kuwait demonstrate advanced stages of digitization with widespread Internet access, while Tunisia’s progress is varied. -
OECD Gender Equality in Technology Governance
Director Mackenzie represented the Aula Fellowship and the AI context in conversations at this global conference for equity. We stand together, or we fall.
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Decoding the Diversity: A Review of the Indic AI Research Landscape
This review paper provides a comprehensive overview of large language model (LLM) research directions within Indic languages. Indic languages are those spoken in the Indian subcontinent, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, and Bhutan, among others. These languages have a rich cultural and linguistic heritage and are spoken by over 1.5 billion people worldwide. With the tremendous market potential and growing demand for natural language processing (NLP) based applications in diverse languages, generative applications for Indic languages pose unique challenges and opportunities for research. Our paper deep dives into the recent advancements in Indic generative modeling, contributing with a taxonomy of research directions, tabulating 84 recent publications. Research directions surveyed in this paper include LLM development, fine-tuning existing LLMs, development of corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. We found that researchers across the publications emphasize the challenges associated with limited data availability, lack of standardization, and the peculiar linguistic complexities of Indic languages. This work aims to serve as a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages, and contributes to the development of more accurate and efficient LLM applications for these languages.