AI Governance
AI governance refers to the organizational policies, processes, and oversight mechanisms that ensure ethics, transparency, and accountability in AI system development and operation.
As AI is increasingly used for business decisions, questions like "Why did this AI make this decision?", "Is there bias?", and "Who takes responsibility?" become unavoidable. AI governance is the framework that prepares answers to these questions from both technical and institutional perspectives.
Specifically, it encompasses bias auditing of training data, ensuring explainability of outputs, human-in-the-loop intervention for final decisions, and responsibility allocation during incidents. Frameworks are being developed across regions: the EU AI Act, Japan's AI Business Guidelines, and NIST AI RMF among others.
Organizational adoption requires more than just policy creation—it demands model card management, risk assessment workflow integration, and regular fairness audits. For companies deploying AI in Thailand and ASEAN countries, ensuring alignment with PDPA (Personal Data Protection Act) is also a critical practical concern.
On the technical side, methods for quantitatively detecting training data bias (Fairness Metrics) and visualizing model reasoning (SHAP, LIME) are in practical use, and governance automation is gradually progressing.
Related Terms
AI ROI (Return on Investment in AI)
AI ROI is a metric that quantitatively measures the effects obtained — such as operational efficienc
AI Observability
An operational practice of continuously monitoring and visualizing the inputs/outputs, latency, cost
Ambient AI
Ambient AI refers to an AI system that is seamlessly embedded in the user's environment, continuousl
BPO (Business Process Outsourcing)
BPO refers to a form of outsourcing in which a company delegates specific business processes to an e