Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence: Explainability, ethical issues and bias
10
Zitationen
1
Autoren
2021
Jahr
Abstract
There is no doubt that Artificial Intelligence (AI) is a topic that is attracting increasing attention from different communities, business and academic. AI adoption and implementation is faced by the difficulty of interpreting and trusting the outcomes of AI algorithms.
Ähnliche Arbeiten
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2017 · 20.576 Zit.
Generative Adversarial Nets
2023 · 19.892 Zit.
Visualizing and Understanding Convolutional Networks
2014 · 15.300 Zit.
"Why Should I Trust You?"
2016 · 14.396 Zit.
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
2024 · 13.164 Zit.