Artificial Intelligence and Ethics
Notice bibliographique
Résumé
The use of artificial intelligence (AI) in various fields of society has increased significantly in recent years. However, as AI systems become more advanced, ethical considerations that arise must be addressed. The chapter “Artificial Intelligence and Ethics” part of the book Digital Health Entrepreneurship provides a comprehensive overview of the ethical implications surrounding the use of AI in society. The chapter begins by defining ethics as a system of moral principles that guide human behavior, highlighting the need for these principles to guide the development and deployment of AI. It provides a detailed overview of AI, including its architectural structures, learning algorithms, and reliance on various types of data. The chapter identifies potential ethical challenges associated with AI, including autonomy loss, bias, deception, deep fakes, discrimination, erosion of society, exclusion, humane treatment of AI, incompetence, inequality, lethal autonomous weapons, malicious use, privacy violations, safety concerns, security risks, transparency loss, and unintended consequences. To address these ethical challenges, the authors call for action to engage the global community in ongoing discussions and initiatives focused on ethical AI. The chapter observes convergence around key ethical principles of transparency, justice and fairness, non-maleficence, responsibility, and privacy. The United Nations and the World Health Organization offer perspectives on ethical AI, emphasizing human-centered, safe, trustworthy, beneficial, transparent, responsible, explainable, interpretable, and meaningful AI. The ethical considerations surrounding AI have implications for a wide range of human stakeholders, including researchers, policymakers, industry leaders, and the public. Interdisciplinary collaboration is needed among experts in diverse fields. Additionally, engaging the public in these discussions is essential to ensure that AI is developed and deployed in ways that align with societal values and expectations. The chapter concludes by stressing the importance of integrating ethical considerations into AI development and deployment. It highlights the need for a universal global standard on ethical AI and the significance of collaboration among nations, organizations, and entities worldwide. By prioritizing ethics in AI, societies can ensure the responsible and beneficial use of this transformative technology for the well-being and safety of humanity. In summary, the chapter “Artificial Intelligence and Ethics” part of the book Digital Health Entrepreneurship published by Springer Nature offers valuable insights into the ethical implications of AI. It emphasizes the importance of a human-centric approach to AI development and deployment, highlighting the need for safety, fairness, transparency, accountability, and inclusivity. The chapter identifies potential ethical challenges associated with AI and offers solutions to address these challenges. Ongoing interdisciplinary approaches and international collaboration are crucial in navigating the complex ethical landscape of AI and ensuring its responsible and beneficial use for the betterment of humanity.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».