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Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring.

Though it’s challenging to set policies for a technology that is so young and still in flux, the need is urgent. Because usage of AI presents both opportunities and risks, libraries must be proactive. The WEF (World Economic Forum) predicts that while there may be a loss of 92 million jobs to various AI tools, they see 170 million new jobs and roles emerging from the use of this new technology. Whether or not this proves correct, there are already digital literacy skills, including a basic understanding of the technology around AI, that libraries can help their patrons master; and because of data privacy, security, and ethics issues, library workers require clear AI policies too. Technology expert Hastings highlights the first attempts of libraries across the country to set up policies for the use of AI in their organizations, as well as policies created by cities and other similar institutions, to guide stakeholders in creating their own smart, forward-thinking course of action. Succinct and pragmatic, this timely resource 

• provides a short overview of Generative AI history, basic technical knowledge, and the key arguments put forth by those in favor and those opposed to this technology;
• offers advice on discovering how library staff encounter AI in their day-to-day work, both in their own usage and by patrons, to inform what policies the library needs and which policies to consider adjusting for particular use cases;
• includes separate chapters focusing on staff-side policy and patron/public-facing policy, delving into essential considerations like staff training, privacy and data protection, security, AI usage for reference, patron education and AI literacy, and setting limits and boundaries on patron AI use;
• suggests useful approaches to ensure that AI policies are easy to follow and understand by library workers, patrons, and the wider community; 
• discusses how to craft flexible procedures that can be updated as AI continues to change, drawing on technology management best practices; and
• offers links to additional resources like sample policy documents and a curated reading list for understanding the environment around AI. 

Critical Data Storytelling in the Composition Classroom provides a timely and essential framework for integrating data literacy into multimodal composition pedagogy. Angela Laflen demonstrates that in an era dominated by big data and AI, the need to understand how to work with data is no longer limited to scientists and mathematicians. Instead, data literacy has become a crucial skill for participating in democratic society.
 
At the heart of Laflen’s approach is critical data storytelling—a practice that equips students with the skills to understand, interpret, and ethically communicate with and about data through various multimodal formats. By teaching students to make informed decisions as data storytellers, Laflen addresses the ethical implications of working with data while offering practical strategies for reading and analyzing data stories. This approach empowers both students and teachers to engage critically with data as a tool for learning and communication. It also highlights how multimodal composition has yet to fully account for the central role of data in shaping contemporary communication and argumentation.
 
By focusing on the ethical and rhetorical dimensions of data storytelling, Critical Data Storytelling in the Composition Classroompresents a pedagogical approach that prepares students for the challenges of working with data in a rapidly evolving digital landscape. This flexible, adaptable model for teaching critical data literacy is of great interest to writing instructors, scholars in rhetoric and composition, and educators who seek to prepare students for the demands of a data-driven world.
 
 
 

Intelligent healthcare systems refer to the use of technologies, such as IoT, big data, AI, and machine learning, to improve the quality and efficiency of healthcare delivery. Healthcare data is both valuable and sensitive, and patients' data is increasingly at risk from cyber attacks on medical devices and critical healthcare infrastructure, posing serious risks to patient health and safety or compromising sensitive user data. As healthcare provision relies upon numerous interconnected systems, to facilitate the diagnosis or treatment of patients' health conditions, these systems rely on advanced cryptography to secure and protect sensitive user information.

Intelligent healthcare systems refer to the use of technologies, such as IoT, big data, AI, and machine learning, to improve the quality and efficiency of healthcare delivery. Healthcare data is both valuable and sensitive, and patients' data is increasingly at risk from cyber attacks on medical devices and critical healthcare infrastructure, posing serious risks to patient health and safety or compromising sensitive user data. As healthcare provision relies upon numerous interconnected systems, to facilitate the diagnosis or treatment of patients' health conditions, these systems rely on advanced cryptography to secure and protect sensitive user information.