The Pivotal Role of Libraries in Sustainable AI Development
Bibliographic record
Abstract
Artificial Intelligence (AI) has gained exceptional public and media coverage since the launch of the ChatGPT platform, a generative conversational intelligence, in November 2022. Nevertheless, AI is already an integral part of our digital daily lives, as we navigate through social networks, use our GPS, or consult recommendations on e-commerce websites. Due to its pervasive influence across all sectors of our societies, AI is gradually becoming a pivotal subject in terms of regulation, societal direction, and legislation. As early as 2021, UNESCO published a report presenting avenues for ethical considerations in AI. In June 2023, the European Union also established a regulatory framework outlining requirements and obligations for AI usage. As a digital manifestation and given the "new ways in which its use influences human thinking, interaction and decision-making and affects education, human, social and natural sciences, culture, and communication and information" (UNESCO. General Conference, 41st, 2021), public libraries have a role to play in enabling residents within their communities to grasp this technology. Their role is all the more significant as AI generates concerns and distrust (Gillath et al., 2021) among populations when "libraries also continue to enjoy a high level of trust and appreciation in most of their communities" (Arlitsch \& Newell, 2017). Understanding AI thus constitutes a new cornerstone for accessing the necessary information to advance sustainable development, as outlined in the Lyon Declaration (2014). Moreover, comprehension of AI aligns with the ethical concerns articulated by UNESCO in terms of explainability and transparency and aligns with several Sustainable Development Goals (SDGs) of the 2030 Agenda. These goals include quality education (4), industry, innovation and infrastructure (9), reduced inequalities (10), sustainable cities and communities (11), as well as responsible consumption and production (12). The role of public libraries in advancing the goals of the 2030 Agenda is beyond dispute (IFLA, 2016), and various digital literacies are already integral to their actions. As AI is predominantly developed by global economic giants and permeates all of our practices, "Shouldn’t [libraries] be the bastions of information literacy and information privacy in an AI world?" (Cox et al., 2018). Thus, to what extent can public libraries take on this subject to promote and offer relevant literacy? For this study, we will conduct a cross-analysis among three European countries—Spain, France, and Italy—to provide insights into the diverse ways in which AI influences professional practices. Through a literature review and semi-structured interviews, the objective is to delineate the challenges of Artificial Intelligence within the framework of the Agenda 2030 program. Subsequently, we will delve into the specificity of AI Literacy in comparison to Information Literacy, a practice already adopted by libraries. Finally, we will analyze the current and prospective role of AI in libraries to propose avenues for implementing concrete actions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.059 | 0.033 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".