This is the conclusion I’ve come to after witnessing the growing popularity of AI tools. AI is everywhere, despite its propensity to produce misleading or incorrect information, and the moral objections of workers who fear losing their livelihoods. Why do patrons choose AI tools while more reliable (and less controversial) options sit on the shelf?
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Among the most recent AI developments with the potential to significantly transform library services is tiny machine learning (TinyML). TinyML is a low-latency technology that enables intelligent, on-device decision-making without requiring data transmission to the cloud for processing.
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We must remember that those who profit the most from our growing reliance of GenAI are the tech companies themselves. Meanwhile, the people who are the most excited about AI are the ones who understand it the least. While machine learning can be useful, I argue that GenAI comes at an unacceptable cost. Taking in to consideration GenAI’s role in the spread of disinformation, the complex damages caused to people and the planet along with the proven negative effect to cognitive skills among users, this text advocates for critical perspectives, and ideally, critical refusal of GenAI.
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How should people and AI work together in ways that are useful, ethical, and trustworthy? Edited by Dan Wu and Shaobo Liang (Wuhan University), “Human–AI Interaction and Collaboration” maps the fast-moving terrain where users, systems, and information meet—treating human strengths and machine strengths as complements, not substitutes. The introduction frames collaboration as a user-centered endeavor that must balance capability with ethics, transparency, and trust.
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With Artificial Intelligence’s (AI) seemingly increasing integration into various aspects of society, nations worldwide—including Tribal Nations—are assessing its impact on the changing landscape. AI is a revolutionary technology that poses potential opportunities and risks for federally recognized Indian Tribes (Tribal Nations or Tribes) and their citizens. This article provides an overview of the literature related to AI in a tribal context.
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In this paper, we explore successful AI training initiatives, resources and networks for librarians, AI integration strategies, and implications of AI for library services. Join me in discovering best practices for preparing librarians as leaders in the generative AI era and integrating AI into library services.
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Modern enterprises are increasingly vulnerable to cyberattacks through their third-party vendors, with over half of all data breaches now originating in the supply chain. Traditional third-party risk management (TPRM), reliant on manual, point-in-time assessments like annual questionnaires, is ill-equipped to address the dynamic nature of cyber threats. This paper proposes an AI-powered TPRM framework as a transformative solution. By enabling continuous, real-time monitoring of vendor security posture – AI streamlines risk assessments using intelligent questionnaires and provides dynamic, multi-factor risk scoring to prioritize critical vendors. The integration of AI into TPRM represents a paradigm shift from a static, reactive process to a dynamic, intelligent, and continuous safeguard, essential for security in an interconnected digital ecosystem.
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In traditional information systems, preservation is paramount, deletion represents data loss. MemGPT challenges this paradigm by implementing strategic forgetting through two key mechanisms: summarization and targeted deletion.
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When a harsh peer review exposed systemic bias, the author drew on established and emerging research to envision AI‑powered Review Reveal: a tool that flags exclusionary reviewer language, maps critiques to manuscript sections, audits equity‑related phrasing, and suggests inclusive rewrites — protecting intellectual freedom and making global digital knowledge culture more accessible to under‑represented and all participating scholars.
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This article explores how bias in artificial intelligence (AI) systems affects healthcare outcomes for South African patients. It highlights real-world examples, including the inaccuracy of pulse oximeters on darker skin and the disproportionate targeting of Black healthcare providers by fraud detection algorithms. Drawing on case studies and policy developments, including South Africa’s National AI Policy Framework, the article examines how biased data can reinforce inequality in medical decision-making. It calls for inclusive data practices, transparent algorithm design, and ethical oversight to ensure AI technologies serve all South Africans fairly and effectively.
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