Explainable Artificial Intelligence for Transparent and Trustworthy AI

Published December 20, 2025 | Version v1

Conference paper Open

Explainable Artificial Intelligence (XAI) has

emerged as a crucial area of research, addressing the opaque

nature of deep learning models, which is particularly problematic

in high-stakes fields that necessitate interpretability and trust,

such as healthcare, finance, and autonomous systems. This

review delineates the progression of XAI, with an emphasis

on recent advancements, as well as the distinctions between

model-specific and model-agnostic methodologies, while critically

examining the challenges inherent in reconciling accuracy with

transparency. Prominent XAI techniques are systematically

discussed, encompassing feature attribution, visual explanations,

and both local and global interpretability strategies. A

comparative analysis of the applicability and limitations

of these techniques within deep learning architectures is

provided. Moreover, this paper evaluates training strategies

and architectural modifications that are intended to enhance

interpretability in neural networks without compromising their

performance metrics. A thorough overview of contemporary

applications illustrates the integral function of XAI in promoting

ethical AI practices and ensuring compliance with regulatory

standards. Ultimately, this review aspires to inform future

research initiatives by highlighting promising avenues for the

development of AI systems that are not only interpretable and

robust but also socially responsible.

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