Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

As technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms increasingly take over personal computing online and public sector domains, they simultaneously raise the promise of an extensively productive and sustainable future, as well as fears of widening inequalities, information and content divide, and a more complex information-seeking landscape. Thus, the hopes of improved accuracy, efficiency, productivity, reduced human bias in decision-making, and access to information are fast giving way to a trove of ethical and human rights issues with far-reaching consequences for accountability, privacy, social justice, equity, inclusion, and informed consent, and public participation in decision-making. Since no technology is entirely free of bias, this paper identifies algorithmic fairness as a more realistic threshold and goal. Building on findings from a previous PRISMA review of relevant literature, the paper proposes a comprehensive framework for defining algorithmic fairness in the context of information access.

This paper reports on an audit study of generative AI systems (ChatGPT, Bing Chat, and Perplexity), which investigates how these new search engines construct responses and establish authority for topics of public importance. We collected system responses using a set of 48 authentic queries for 4 topics over 7 days, and analyzed the data using sentiment analysis, inductive coding, and source classification. Results provide an overview of the nature of system responses across these systems, and provide evidence of sentiment bias based on the queries and topics, and commercial and geographic biases in sources. The quality of sources used to support claims is uneven, relying heavily on News and Media, Business and Digital Media websites. Implications for system users emphasize the need to critically examine Generative AI system outputs when making decisions related to public interest and personal well-being.

By examining loan data from public libraries in South Korea, we seek to understand patterns in user borrowing behaviors and explore thematic connections among borrowed books. The subject headings of 55.5 million book sets borrowed by individual users on the same day were analyzed using ITEM2VEC. We have identified 40 subject heading communities through cosine similarity of each subject vector, and we have labeled each community using a large language model. Two prominent communities were identified: Global Modern Literature and Novels and Children’s Literature, Fairy Tales, and Folklore. The latter community was associated with a diverse array of subjects, indicating an expansion in children’s reading preferences. The study results will be useful for improving collection development and the relevance of book recommendations, as well as for incorporating user information behavior into traditional library material classification schemes.

Open access (OA) publishing has been touted as an equalizer to access. However, English has effectively attained a status of lingua franca in science, and the extent to which OA supports cross-language dissemination and consumption of information beyond English is not well understood. This preliminary work investigates English-language sources as referenced in OA articles across 8 world languages in the four most common subject areas of study (i.e. Medicine; Biochemistry, Genetics, and Molecular Biology; Engineering; and Social Sciences) as indexed in Scopus in 2023. Non-English languages and language families analyzed (i.e., Chinese, Spanish, Portuguese, and Russian) consistently included references to English language sources. Further, English-language articles strongly favored providing references to other English-language sources, with 17 out of 20 English-language articles exclusively containing English-language references. Future work will extend the current investigation, while considering the affordances and disadvantages of Scopus to access non-English OA articles.