Navigating the ethical quagmire: unraveling the intricate landscape of ai and machine learning
Notice bibliographique
Résumé
Artificial Intelligence (AI) is rapidly advancing, reshaping industries, economies, and societies. As AI systems become integral parts of our daily lives, questions surrounding morality, ethics, and bias have gained prominence. The intersection of these concepts in AI development raises critical issues that demand careful consideration and responsible governance. The advent of AI introduces a myriad of moral considerations, primarily centered around the ethical treatment of sentient beings, privacy, and the potential impact on employment. Concerns arise when AI algorithms are employed in decision-making processes that affect individuals' lives, such as in healthcare, criminal justice, and finance. Ensuring that AI aligns with human values, respects privacy, and promotes equity is imperative to uphold a moral framework in its development and deployment. The ethical dimensions of AI involve navigating complex decisions that balance benefits and potential harms. Issues such as transparency, accountability, and fairness come to the forefront. Ethical AI design should prioritize transparency to allow users to understand how algorithms make decisions. Accountability mechanisms must be in place to address errors or biases, holding developers and organizations responsible for the outcomes of AI systems. Additionally, achieving fairness in AI is challenging due to biased datasets and algorithms, requiring continuous efforts to mitigate and rectify these biases. On the other hand, Bias in AI systems is a pervasive issue, often stemming from biased training data or the algorithms themselves. Biases can manifest in various forms, including racial, gender, and socio-economic biases. When AI systems learn from historical data that reflects societal prejudices, they perpetuate and even exacerbate existing biases. Mitigating bias requires a comprehensive approach, involving diverse and inclusive datasets, algorithmic transparency, and ongoing scrutiny to identify and rectify biases as they emerge. To address these challenges, interdisciplinary collaboration is essential, bringing together experts from diverse fields such as computer science, ethics, sociology, and law. Stakeholders, including governments, industry players, and the public, must actively engage in shaping ethical guidelines and regulations. Developing frameworks for responsible AI, promoting transparency in algorithmic decision-making, and establishing clear accountability mechanisms are crucial steps towards mitigating bias and ensuring ethical practices in AI development and deployment. Striking a balance between technological innovation and ethical responsibility is paramount for the sustainable and equitable integration of AI into society. Addressing bias and ethical concerns requires collaborative efforts, emphasizing transparency, accountability, and fairness. As AI continues to evolve, it is imperative that we proactively shape its trajectory, ensuring that it aligns with human values and contributes positively to the betterment of society. This research paper using a qualitative method delves into the intricate ethical landscape of AI, highlighting the challenges it presents and proposing potential solutions, offering a comprehensive exploration of the critical ethical dimensions inherent to AI and Machine Learning (ML). Received on: 10 May 2025 Accepted on: 27 August 2025 Published on: 08 September 2025
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 | 0,046 | 0,069 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,013 | 0,070 |
| Communication savante | 0,029 | 0,043 |
| Science ouverte | 0,003 | 0,015 |
| Intégrité de la recherche | 0,011 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».