This white paper addresses the governance and risk control of AI systems, stressing that ethical, legal, and technical safeguards must be embedded across the lifecycle. It highlights risks such as bias, lack of transparency, and systemic failures, while emphasizing accountability, human oversight, and resilience. Key measures include robust data governance, explainability, continuous monitoring, and incident response frameworks. The paper calls for aligning practices with international standards and regulatory frameworks like the EU AI Act. By integrating responsible design and operational controls, organizations can foster trustworthy AI while reducing legal, reputational, and societal risks.

Join for free to read