Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two...

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Hlavní autoři:	Daniele Pelosi, Diletta Cacciagrano, Marco Piangerelli
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-07-01
Edice:	Algorithms
Témata:	explainability explainable AI interpretability interpretable AI concept drift data drift
On-line přístup:	https://www.mdpi.com/1999-4893/18/7/443
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