As generative AI tools move into library makerspaces, they do more than draft metadata or translate text—they reflect the power structures embedded in the spaces where they are deployed. Without intentional design, we risk automating the very inequities libraries seek to dismantle.
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People can experience the same public policy very differently. Some feel their lives are improving; others feel left behind. This is not simply disagreement, it reflects a core part of policy impact that is hard to capture with objective indicators alone: public perception. Traditional social surveys are designed for this purpose, but they are often slow, expensive, and hard to adapt quickly. They also face challenges such as fixed question formats, limited flexibility, and cross-cultural comparability.
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In the classic 1950s TV sitcom I Love Lucy, when Lucy did something outrageous her husband Ricky would exclaim “Lucy, you’ve got some explainin’ to do!” Typically, Lucy would come up with some sort of implausible response. Hilarity ensued. Well, it’s not the 1950s anymore but 70+ years later Large Language Models (LLMs) and AI chatbots (e.g., ChatGPT, Gemini) are doing outrageous things (hallucinations, fabrications, misinformation, and worse) and the explanations, if there are any, are just as implausible. And it isn’t funny.
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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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Can AI have a conscience? Of course, today’s AI isn’t a sentient being with feelings or guilt. It won’t lose sleep over a tough decision. But as artificial intelligence plays a bigger role in our lives, we do expect it to act responsibly. In essence, we want AI to follow ethical principles, a sort of programmed “conscience” so that it helps society without harming it. This is the crux of AI ethics, an increasingly important topic now that machine learning systems are making decisions that matter.
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In the world we are living in, a digital world, some data slips past our awareness, but very little data ever truly disappears. As we, information scientists, are concerned with reproducibility and responsibility of research, data lifecycle models have been developed to manage the complexity. To foster open, transparent, and collaborative science, data is often archived in a repository at the end of the project according to such data lifecycle models. This is often followed by the last step of the lifecycle models, data reuse. Traditionally, this model is cyclical, with reused data leading to new questions and fueling subsequent rounds of research.
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In this episode of InfoFire, I sit down with Professor Mike Thelwall, a well accomplished scholar of Informetrics, to explore the intersections of Large Language Models (LLMs) and research evaluation. We delve into how LLMs are reshaping the landscape of research assessment, examining the promises they hold and the challenges they present in ensuring fair, meaningful, and context-aware evaluations.
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AI (Artificial Intelligence) is coming at humanity fast and furious. Shalini Urs discusses the new epoch of AI with one of the fathers of the Internet, Vint Cerf.
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ChatGPT is a simple-to-use conversation agent developed by OpenAI. Of course, this is not the first time we have seen an AI agent that generates information. Google’s LaMDA and Meta’s Galactica are some recent examples for text generation.
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With data as the infrastructure, and machine learning and deep learning as the technology foundations of building AI systems combined, they largely determine the performance, fairness, robustness, reliability, and scalability of AI systems.
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