Ethics of Artificial Intelligence
Notice bibliographique
Résumé
this discussion.A study by ARL, CNI, and EDUCAUSE over the next 18 months seeks to shed light on the critical technologies shaping research and learning (including AI), and the implications for research libraries as collaborative partners in the research enterprise.9 AI Ethics Initiatives in 2019There are significant AI ethics initiatives underway in 2019 both globally and nationally.This is a critical and opportune time for research libraries to assess and actively engage in informing the principles and practices of AI institutionally, in public policy, and in the research and learning community.Following an initial draft in December 2018, in April 2019 the European Commission's High-Level Expert Group on Artificial Intelligence established seven essentials for achieving trustworthy artificial intelligence.The essentials are: human agency and oversight; robustness and safeness; privacy and data governance; transparency; diversity, non-discrimination, and fairness; societal and environmental well-being; and accountability.10 On May 22, 2019, the Organisation for Economic Co-operation and Development (OECD) issued the "Recommendation of the Council on Artificial Intelligence," 11 adopted by 42 countries, including Canada and the United States.On June 9, 2019, the G20 agreed on guiding principles for adopting artificial intelligence.12 Although the principles among these bodies are not identical, they are more similar than different.Recently, France, Germany, and Japan agreed to jointly fund AI research that respects privacy and transparency.13 Closer to our members from Canada and the United States, on May 14, 2019, the Canadian Minister of Innovation, Science and Economic Development announced the Advisory Council on Artificial Intelligence.The purpose is to "advise the Government of Canada on building Canada's strengths and global leadership in AI, identifying opportunities to create economic growth that benefits all Canadians, Research Library Issues 299 -2019 research to decision-makers in the private and public sectors, in order to advance the use of AI for public good.21 Several institutions of higher education offer AI ethics courses to undergraduates and graduate students; examples include Stanford University, 22 Vanderbilt University, 23 University of Arizona, 24 as well as courses through edX, Coursera, and Udacity.Computer science program accreditation by ABET requires understanding of professional, ethical, legal, security, and social issues and responsibilities-including as ethics relate to AI. 25 In fact, an "AI ethics" online course search in Google returns over 35,000 results. AI Ethics and Research LibrariesIn this global and national context of AI investments and adoption, this issue of RLI focuses on the relation of AI ethics and the role and potential roles of research libraries.A limited sampling highlights the broadening adoption of AI in research libraries.The University of Oklahoma 26 and other examples are highlighted in a 2019 issue of Library Technology Reports edited by Jason Griffey that opens with this compelling statement: "This issue of Library Technology Reports argues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace."27 Now is the time for research libraries to collectively understand and address a host of ethical questions for research institutions, public policy, and more specifically for research library leaders in institutional and public policy, so that research libraries will continue to serve as trusted advisors to our users, and as responsible collectors, disseminators, and preservers of knowledge.To help frame our thinking, we invited three individuals to share their expertise and recommendations with us.
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 | 0,012 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,027 |
| Communication savante | 0,011 | 0,007 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».