Explainability Requirements for AI Decision-Making in Regulated Sectors

The increasing deployment of artificial intelligence (AI) systems in regulated sectors such as finance, public administration, healthcare, pharmaceuticals and cloud-based business intelligence has intensified regulatory and societal demands for explainability in automated decision-making. As AI-driven decisions increasingly affect legal rights, economic outcomes and public trust, explainability has emerged as a foundational requirement for accountability, transparency and lawful governance rather than a purely technical feature. This article examines how legal frameworks, regulatory instruments and ethical principles shape expectations for interpretable and transparent AI systems, situating explainable AI (XAI) within evolving governance regimes including sector-specific compliance obligations and cross-jurisdictional initiatives. It clarifies the distinction between technical interpretability, meaningful explanations for affected stakeholders and legally actionable transparency, and proposes a structured understanding of explainability requirements that aligns technical design choices with regulatory expectations. The findings underscore that explainability must be context-sensitive, proportionate to risk and embedded across the AI lifecycle to ensure trustworthy and compliant AI deployment.