Category: Research Topic: Tech & Society (STS)

Research Topic: Tech & Society (STS)

  • Oui, mais je LLM !

    Oui, mais je LLM !

    L’IA générative nous joue des tours, en manipulant notre perception de la vérité en tentant de devenir notre confident et en créant une relation de dépendance. Mais, on peut aussi à notre tour l’utiliser pour extraire des informations privilégiées mal sécurisées, en utilisant des tactiques adaptées de l’ingénierie sociale.

    Le manque d’expérience autour de cette technologie et l’empressement à en mettre partout expose à de nouveaux risques.

    Je te présente un survol des concepts de base en cybersécurité revisités pour l’IA générative, différents risques que posent ces algorithmes et différents conseils de prévention pour bien les intégrer dans nos systèmes informatiques et notre pratique professionnelle.

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  • The Architecture of Responsible AI: Balancing Innovation and Accountability

    The Architecture of Responsible AI: Balancing Innovation and Accountability

    Artificial Intelligence (AI) has become a key factor driving change in industries, organizations, and society. While technological capabilities advance rapidly, the mechanisms guiding AI implementation reveal critical structural flaws (Closing the AI accountability gap). There lies an opportunity to architect a future where we can collaboratively design systems that leverage AI to augment human capabilities while upholding ethical integrity.

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  • Work in Progress: Exclusive Rhetoric in AI Conference Mission Statements

    Work in Progress: Exclusive Rhetoric in AI Conference Mission Statements

    AI conferences are pivotal spaces for knowledge exchange, collaboration, and shaping the trajectory of research, practice, and education. This paper presents preliminary findings from an analysis of AI conference mission statements, investigating how their stated goals affect who is welcomed into AI conversations. We find that many mission statements reflect assumptions that may unintentionally narrow participation and reinforce disciplinary and institutional silos. This limits engagement from a broad range of contributors—including educators, students, working professionals, and even younger users —who are essential to a thriving AI ecosystem. We advocate for clearer framing that supports democratizing and demystifying AI. By broadening participation and intentionally fostering cross-sector and interdisciplinary connections, AI conferences can help unlock more innovation.

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  • Keeping Players Hooked: Story-Driven iGaming Ecosystem

    Keeping Players Hooked: Story-Driven iGaming Ecosystem

    This GitHub project explores how to:

    ✅ Build modular narrative systems that expand over seasons and quests. ✅ Design story-powered payment systems that turn transactions into experiences. ✅ Grow sustainable gaming enterprises around live storytelling, community co-creation, and ethical monetization. ✅ Create ecosystems where players return not out of compulsion, but love for the story.

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  • Towards Real Diversity and Gender Equality in Artificial Intelligence

    Towards Real Diversity and Gender Equality in Artificial Intelligence

    This is an Advancement Report for the Global Partnership on Artificial Intelligence (GPAI) project “Towards Real Diversity and Gender Equality in Artificial Intelligence: Evidence-Based Promising Practices and Recommendations.” It describes, at a high level, the strategy, approach, and progress of the project thus far in its efforts to provide governments and other stakeholders of the artificial intelligence (AI) ecosystem with recommendations, tools, and promising practices to integrate Diversity and Gender Equality (DGE) considerations into the AI life cycle and related policy-making. The report starts with an overview of the human rights perspective, which serves as the framework upon which this project is building. By acknowledging domains where AI systems can pose risks and harms to global populations, and further, where they pose disproportionate risks and harms to women and other marginalized populations due to a lack of consideration for these groups throughout the AI life cycle, the need to address such inequalities becomes clear.

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  • Yakshi: A Transmedia Narrative Exploration

    Yakshi: A Transmedia Narrative Exploration

    At Smart Story Labs, we are excited to announce a new GitHub project that dives into the transmedia narrative of Yakshi – reimagining this South Asian folklore spirit as a lens to explore cross-cultural storytelling, feminist hauntings, and ecological narratives.

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  • IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding

    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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  • Towards regulating AI : A natural, labour and capital resources perspective

    Towards regulating AI : A natural, labour and capital resources perspective

    Policymakers who are looking at artificial intelligence (AI) applications are thinking about what we as a society want to achieve and what we need to protect, yet it is not commonly known that AI apps require intensive natural resources, labour and capital.

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  • Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

    Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

    Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models.

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  • Unravelling socio-technological barriers to AI integration: A qualitative study of Southern African newsrooms

    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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  • Advancements in Modern Recommender Systems: Industrial Applications in Social Media, E-commerce, Entertainment, and Beyond

    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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  • GenAI and Religion: Creation, Agency, and Meaning

    GenAI and Religion: Creation, Agency, and Meaning

    This paper explores the parallels between Generative Artificial Intelligence (GenAI) and religious systems in three domains: creation, agency, and meaning-making. Both offer frameworks for human engagement but differ in intent, autonomy, and moral accountability. Despite these differences, GenAI and religion share roles as creators, influencers, and meaning facilitators. We address and counter rebuttals to these parallels, highlighting GenAI’s co-constructed outputs and its impact on modern meaning-making. The paper concludes with the societal implications of these parallels in shaping future thought and action.

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