Building trust in AI through transparency and human oversight
As AI adoption accelerates across industries, trust in its output remains fragile because transparency, accountability, and human oversight have not kept pace with deployment. This article argues that trustworthy AI cannot be achieved through model capability or regulatory compliance alone; it requires explainability that reaches decision-makers, verifiable data provenance, continuous governance contracts, and human checkpoints designed to identify errors, bias, drift, and vendor-risk gaps. By treating transparency and oversight as ongoing operational disciplines rather than one-time compliance exercises, organisations can build AI systems that people are more willing to understand, question, and responsibly rely on.
