The Pivotal Role of Libraries in Sustainable AI Development
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,017 | 0,027 |
| Communication savante | 0,059 | 0,033 |
| Science ouverte | 0,002 | 0,026 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».