Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Rethinking fairness in unsupervised healthcare AI: A methodological scoping review
0
Zitationen
3
Autoren
2026
Jahr
Abstract
Fairness in unsupervised healthcare AI is an emerging but conceptually unsettled field. Current approaches reflect diverse and sometimes incompatible notions of equity, underscoring the need for clearer theoretical grounding. Progress will require explicit articulation of fairness goals, stronger integration of domain expertise and participatory evaluation, and closer alignment between algorithmic fairness criteria and clinically meaningful structures. This review provides a conceptual and methodological foundation to support more rigorous and transparent development of fair unsupervised healthcare AI systems.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.539 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.426 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.921 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.586 Zit.
Autoren
Institutionen
- Université de Versailles Saint-Quentin-en-Yvelines(FR)
- Université Paris-Saclay(FR)
- Assistance Publique – Hôpitaux de Paris(FR)
- Hôpital Raymond-Poincaré(FR)
- Centre National de la Recherche Scientifique(FR)
- Inserm(FR)
- Université de Lille(FR)
- Centre Hospitalier Universitaire de Lille(FR)
- École Centrale de Lille(FR)