Unlocking the Black Box: Explainable Artificial Intelligence (XAI) for Trust and Transparency in AI Systems
A conceptual review explores the significance of XAI in promoting trust and transparency in AI systems, analyzes existing literature on XAI, identifies patterns and gaps, and presents a coherent conceptual framework.
Abstract
Explainable Artificial Intelligence (XAI) has emerged as a critical field in AI research, addressing the lack of transparency and interpretability in complex AI models. This conceptual review explores the significance of XAI in promoting trust and transparency in AI systems. The paper analyzes existing literature on XAI, identifies patterns and gaps, and presents a coherent conceptual framework. Various XAI techniques, such as saliency maps, attention mechanisms, rule-based explanations, and model-agnostic approaches, are discussed to enhance interpretability. The paper highlights the challenges posed by black-box AI models, explores the role of XAI in enhancing trust and transparency, and examines the ethical considerations and responsible deployment of XAI. By promoting transparency and interpretability, this review aims to build trust, encourage accountable AI systems, and contribute to the ongoing discourse on XAI.