Unveiling explainability in artificial intelligence: a step to-wards transparent AI

Abstract

Explainability in artificial intelligence (AI) is an essential factor for building transparent, trustworthy, and ethical systems, particularly in ‎high-stakes domains such as healthcare, finance, justice, and autonomous systems. This study examines the foundations of AI explainability, ‎its critical role in fostering trust, and the current methodologies used to interpret AI models, such as post-hoc techniques, intrinsically inter-‎pretable models, and hybrid approaches. Despite these advancements, challenges persist, including trade-offs between accuracy and inter-‎pretability, scalability, ethical risks, and transparency gaps. The paper explores emerging trends like causality-based explanations, neuro-‎symbolic AI, and personalized frameworks, while emphasizing the integration of ethics and the need for automation in explainability. Future ‎directions stress the importance of collaboration among researchers, practitioners, and policymakers to establish industry standards and ‎regulations, ensuring that AI systems align with societal values and expectations.

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