The emergence of generative AI and Large Language Models (LLMs) has sparked heated discussions around their ethical usage. The stakes are higher when it comes to the ethical use of generative AI in high schools. High schoolers are the next generation’s decision-makers, and educating them on ethical generative AI use in academia is crucial.
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AI is a critical tool in addressing complex global issues, including climate change, by analyzing vast datasets and modeling potential solutions. However, this journey towards harnessing AI’s potential is accompanied by a pressing concern: the proliferation of misinformation generated by AI systems, such as fabricating references by ChatGPT.
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Managing traditional cultural expressions (TCEs) within library settings can be cumbersome for any librarian, and is especially challenging for those lacking adequate training to handle the complex legal and ethical issues involved in collection development and managing collections including TCEs. Below, we explore intersections between TCEs and librarianship, including current issues and potential remedies within LIS education.
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For many people, tattoos are a powerful and beautiful way to adorn the body and express identity, memory, ideas or artistic preferences. For some, tattoos are symbols of trauma and regret. Whether tattoos simply decorate the body or carry a deeper meaning, information is created, captured and communicated through tattoo images and during the tattooing process. Researchers at Victoria University of Wellington in New Zealand have been exploring the informational aspect of tattoos in a project called Tattoo and Information. We interviewed 25 New Zealanders of diverse profiles. Here is what we know so far.
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Information science is well-positioned to lead and critically inform the development of theoretical and technological frameworks that support information integrity and academic integrity. If we agree with the argument that generative AI tools and technologies share such high-level facets as people, information, data, technology, and their interaction with information science, then it is our responsibility to embrace the opportunities and address the emerging informational, social, ethical, and cultural challenges.
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Not only is it becoming increasingly difficult to distinguish fact from opinion, but also to understand why certain content ends up in our feeds, recommendations or search results in the first place. Yet it’s more important than ever to understand it. This is where infrastructural meaning-making comes into play, and it’s something that the datafied society needs to understand.
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If we think about phrases, cliches, or old adages in the English language, many of them are rooted in a color, or the lack thereof. Many of these phrases also speak to the phenomenon of disinformation, its spread, and its entrenchment in our daily lives. This short essay will introduce several such colorful phrases.
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Social Informatics (SI), in its broadest sense, is an approach for understanding interactions between various societal systems and information-communication technologies.
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The integration of the Bekenstein bound and Landauer’s principle represents a milestone in comprehending the limits and principles governing information and energy. By weaving these concepts together, we illuminate pathways that span various disciplines, enriching our comprehension of the natural world’s intricacies.
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We examined the reliability of image recognition services by conducting a cross-comparison across three popular image recognition services: Google Vision AI, Microsoft Azure Computer Vision and AWS Rekognition.
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