Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance

Post hoc explanations for black-box machine learning models have been criticized for potentially inaccurate surrogate models and computational burden at prediction time. We propose pre hoc and co hoc explainability frameworks that integrate interpretability directly into the training process through...

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Bibliografische Detailangaben

Hauptverfasser:	Cagla Acun, Olfa Nasraoui
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2025-07-01
Schriftenreihe:	Applied Sciences
Schlagworte:	XAI explainability in machine learning local explainability global explainability
Online-Zugang:	https://www.mdpi.com/2076-3417/15/13/7544
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