Conference Agenda (All times are shown in Mountain Daylight Time)

Few topics are more often discussed than surveillance, particularly in the context of surveillance technologies that reflect structural inequities. There is space, however, to bring more discussion of surveillance tech into the library literature. At the same time, literature on digital surveillance and associated systems such as Big Data, surveillance capitalism, and platform capitalism often discuss these phenomena as if they are novel rather than iterations of long-standing inequitable circumstances. We propose that a dialogue between surveillance literature and critical library literature will benefit both areas: theories from the surveillance domain can strengthen examinations of structural oppression in libraries while theories from critical library literature can strengthen acknowledgment of surveillance techs’ historical roots. Moreover, overlap exists between concepts used in surveillance and library literature, including concerns about neutrality and classification practices. Therefore, after reviewing surveillance theories and their applicability to libraries, we demonstrate how these scholarly areas may strengthen each other, with three major consequences: (a) moving library literature beyond considerations of the panopticon in favor of the surveillant assemblage; (b) recognizing that surveillance tech is a hyper-visible form of historical oppression; and (c) acknowledging that the library ethos is critical to any fight for justice within information science.

Artificial Intelligence (AI) is at the top of the agenda for education leaders today in educating the next generation across the globe. However, public understanding of AI technologies and how to define AI literacy is under-explored. This vision poses upcoming challenges for our next generation to learn about AI. On this note, an exploratory review was conducted to conceptualize the newly emerging concept “AI literacy”, in search for a sound theoretical foundation to define, teach and evaluate AI literacy. Grounded in literature on 18 existing peer-reviewed articles, this review proposed four aspects (i.e, know and understand, use, evaluate, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies. This study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy.

AI language models trained on Web data generate prose that reflects human knowledge and public sentiments, but can also contain novel insights and predictions. We asked the world’s best language model, GPT-3, fifteen difficult questions about the nature, value, and future of library and information science (LIS), topics that receive perennial attention from LIS scholars. We present highlights from its 45 different responses, which range from platitudes and caricatures to interesting perspectives and worrisome visions of the future, thus providing an LIS-tailored demonstration of the current performance of AI language models. We also reflect on the viability of using AI to forecast or generate research ideas in this way today. Finally, we have shared the full response log online for readers to consider and evaluate for themselves.

Artificial Intelligence (AI) systems are embedded in institutions that are not diverse, that are inequitable, unjust, and exclusionary. How do we address the interface between AI systems and an unjust world, in service to human flourishing? One mechanism for addressing AI Ethics is AI Ethics Education: training those who will build, use, and/or be subject to AI systems to have clear moral reasoning, make responsible decisions, and take right actions. This paper presents, as part of a larger project, work on what AI Ethics instructors currently do and how they describe their motivating concerns. I find that although AI Ethics content and pedagogy is varied, there are some common motivating concerns particular to this kind of teaching, which may be useful in structuring future guidance for new AI Ethics teachers, evaluating existing pedagogy, guiding research on new pedagogies, and promoting discussion with the AI Ethics community.