Explainable Generative AI: A Two-Stage Review of Existing Techniques and Future Research Directions
Prabha M. Kumarage; Mirka Saarela · 2026 · AI
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Generative Artificial Intelligence (GenAI) models produce increasingly sophisticated outputs, yet their underlying mechanisms remain opaque. To clarify how explainability is conceptualized and implemented in GenAI research, this two-stage review systematically examined 261 articles retrieved from six major databases. After removing duplicates and applying predefined inclusion criteria, 63 articles were retained for full analysis. In the first stage, an umbrella review synthesized insights from 18 review papers to identify prevailing frameworks, strategies, and conceptual challenges surrounding
Abstract by Prabha M. Kumarage; Mirka Saarela, AI (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/ai7010031