Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating and Reducing AI Model Group Disparity: An Analysis of COVID Test Outcomes in Children
0
Zitationen
3
Autoren
2023
Jahr
Abstract
AI fairness in healthcare has attracted significant attention due to the potential risk of perpetuating health disparity. This study assessed the group parity of a set of machine learning (ML) models trained on the National Health Interview Survey data, with COVID test result as the outcome. We also experimented with the use of synthetic data to reduce group disparity. Our results suggests that group disparity is prevalent in ML models though often not statistically significant, and the use of synthetic data can sometimes enhance group parity.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.250 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.109 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.482 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.434 Zit.