A perspective on the future role of Explainable AI framework in AI governance

Explainable AI (XAI) plays a critical role in AI governance by ensuring transparency, accountability, and trust in AI decision-making processes. XAI involves techniques and tools that help understand and interpret AI models' decisions, making them more explainable and accountable. This paper presents the authors perspective on the significance of XAI in AI governance, demonstrating its capacity to support both legal and ethical frameworks. We identify the roles of critical components of Explainable AI (XAI) framework such as data, algorithm, model, control parameters, predictor methods unveiling the transparency, bias, accuracy and risks associated with AI driven systems. We also highlight the imperative integration of XAI in AI governance for promoting responsible AI development and deployment.

Objectives: This case study primarily examines critical role of AI governance, emphasizing its implementation using explainable AI.

Methodology/Design/Approach: Scholarly publications reviewed in this article are from peer reviewed journals and conferences.

Findings/Result: This study explores the importance of AI Governance with a special focus on implementation using explainable AI.

Originality/Value: The analysis provides literature survey of current works on integration Explainable AI with AI governance.

Keywords: Explainable AI, AI Governance, Artificial Intelligence, Machine Learning, Responsible AI, XAI.