Explainability in Generative Artificial Intelligence: A Two-Phase Review of Current Techniques, Limitations, and Open Challenges - Kumarage, Prabha (2025)
The rapid development of Generative Artificial Intelligence (GenAI) has introduced a new era of technological innovation by revolutionizing how people interact with information. Explainability is a crucial aspect to ensure transparency, accountability, and trust in these GenAI-driven systems. Interpreting and comprehending the decision-making process of GenAI models is becoming increasingly difficult as they become more complex and widespread over time. This research study aims to undertake a thorough navigation of the current state of explainability in GenAI through a two-phase literature review: an umbrella review to analyze previous reviews and an empirical review to examine empirical studies. First, the umbrella review provides an overview by analyzing the existing explainable techniques for GenAI and their limitations. The key limitations in explaining GenAI models include generalization issues, computational inefficiencies, and trade-offs between interpretability and model performance. The empirical review then examines how these GenAI explainable techniques are applied and evaluated in practical settings. A significant finding is the absence of a standardized evaluation framework to measure and compare the effectiveness of different explainability techniques. Finally, the findings of the two reviews are synthesized to identify open challenges and propose potential future directions for improving explainability in GenAI. This study highlights the importance of developing well-balanced GenAI-specific explainable techniques that align with AI regulations to ensure the responsible development of GenAI solutions. In addition, researchers, AI professionals, and policymakers seeking to improve the transparency and explainability of GenAI models can all greatly benefit from the findings.