ASEAN Guide on AI Governance and Ethics
The ASEAN Guide on AI Governance and Ethics provides practical guidelines for organisations designing, developing, and deploying traditional AI technologies in commercial, non-military, and dual-use applications across Southeast Asia. It outlines seven core guiding principles—such as transparency, explainability, fairness, safety, and accountability—and establishes a comprehensive AI governance framework covering internal structures, human oversight, and operations management. Additionally, the document offers strategic recommendations for national policymakers and regional bodies to foster AI innovation, workforce upskilling, and cross-border alignment.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This guide is for organizations, developers, deployers, and policymakers in the ASEAN region seeking practical guidance on designing, developing, and deploying AI responsibly.
What the document contains
The document covers seven foundational principles for ethical AI, an operational framework for internal governance, risk-based human oversight categorization, lifecycle operations management, and stakeholder communication strategies. It also provides actionable recommendations for national governments and regional bodies regarding talent development, innovation ecosystems, and generative AI governance.
The guidelines are voluntary and do not replace or supersede any existing or upcoming national laws, nor do they establish binding legal enforcement mechanisms or mandatory penalties for non-compliance.
- The Guide establishes seven core principles including transparency, fairness, security, human-centricity, privacy, accountability, and robustness to foster trustworthy AI.
- Organizations are advised to implement internal governance structures such as multi-disciplinary ethics committees and clear role definitions.
- Human involvement in AI-augmented decision-making is categorized into human-in-the-loop, human-over-the-loop, and human-out-of-the-loop based on risk assessments.
- Operations management must address the entire AI system lifecycle, emphasizing data quality, bias mitigation, rigorous modeling, and continuous monitoring.
- National and regional recommendations emphasize upskilling the workforce, supporting AI innovation ecosystems, and adapting frameworks for generative AI.
· Executive Summary · A. Introduction · B. Guiding Principles for the Framework · C. AI Governance Framework · D. National-level Recommendations · E. Regional-level Recommendations · F. Conclusion · Annex A: AI Risk Impact Assessment Template · Annex B: Use Cases
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
Deployers should be accountable for decisions made by AI systems and for the compliance with applicable laws
Accountability and Integrity
screening score · 2026-10-23
Fairness
4/5Non-discrimination and equitable treatment
Deployers of AI systems should conduct regular testing of such systems to confirm if there is bias and where bias is confirmed, make the necessary adjustments
Fairness and Equity
screening score · 2026-10-23
Human Dignity
4/5Respect for the inherent worth of persons
AI systems should respect human-centred values and pursue benefits for human society, including human beings’ well-being
Human-centricity
screening score · 2026-10-23
Human Oversight
4/5Humans remain in control of consequential decisions
Three broad categories of human involvement based on level of risk – human-in-the-loop, human-over-the-loop, human-out-of-the-loop
Executive Summary
screening score · 2026-10-23
Privacy
4/5Protection of personal data and private life
AI systems should have proper mechanisms in place to ensure data privacy and protection and maintain and protect the quality and integrity of data
Privacy and Data Governance
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
Transparency refers to providing disclosure on when an AI system is being used and the involvement of an AI system in decision-making
Transparency and Explainability
screening score · 2026-10-23
Enforcement
1/5Consequences and compliance mechanisms with real force
While adoption of the framework laid out herein is voluntary, this Guide can help organisations build trust
Objectives
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Conduct risk-based assessments before starting any data collection and processing or modelling
Executive Summary
screening score · 2026-10-23