Model Artificial Intelligence Governance Framework
MODEL ARTIFICIAL INTELLIGENCE GOVERNANCE FRAMEWORK
The Singapore Model AI Governance Framework translates ethical principles into practical recommendations that organizations can readily adopt to deploy AI responsibly. This second edition incorporates organizational experiences, industry examples, and international feedback to provide guidance across four key areas: internal governance, human involvement in decision-making, operations management, and stakeholder interaction. It is designed to be algorithm-, technology-, sector-, and scale-agnostic, assisting organizations in building stakeholder trust and aligning with accountability-based data practices.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Organizations and enterprises deploying AI solutions at scale who wish to implement responsible and accountable AI governance practices.
What the document contains
The document covers practical governance measures for organizations deploying AI, including internal structures, human oversight in decision-making, data management and operations, and stakeholder communication.
It does not establish a brand-new set of standalone ethical principles, nor does it address legal liabilities, intellectual property rights, data-sharing mechanics, or cyber security threat mitigation.
- The framework translates high-level ethical principles into implementable practices for organizations deploying AI at scale.
- It focuses on four broad areas: internal governance structures, human involvement in decision-making, operations management, and stakeholder interaction.
- The guidelines are algorithm-agnostic, technology-agnostic, sector-agnostic, and scale-agnostic to maximize broad applicability.
- Adoption of the framework is voluntary and aims to help organizations build stakeholder trust and demonstrate accountability.
- Organizations must tailor the framework's measures based on the nature, complexity, and impact of their AI systems.
· Summary of Updates · Foreword · 1. Preamble · 2. Introduction · 3. Model AI Governance Framework
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
demonstrate reasonable efforts to align internal policies, structures and processes with relevant accountability-based practices in data management and protection
Objectives
screening score · 2026-10-24
Fairness
4/5Non-discrimination and equitable treatment
Organisations using AI in decision-making should ensure that the decision-making process is explainable, transparent and fair.
Guiding Principles
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
Introduction
screening score · 2026-10-24
Human Oversight
3/5Humans remain in control of consequential decisions
AI technologies may be used to augment a human decision-maker or to autonomously make a decision.
Objectives
screening score · 2026-10-24
Privacy
3/5Protection of personal data and private life
align internal policies, structures and processes with relevant accountability-based practices in data management and protection (e.g. the Personal Data Protection Act 2012 (“PDPA”) and the OECD Privacy Principles)
Objectives
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
Organisations using AI in decision-making should ensure that the decision-making process is explainable, transparent and fair.
Guiding Principles
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
Introduction
screening score · 2026-10-24
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Adopting this Model Framework will require tailoring the measures to address the risks identified for the implementing organisation.
Objectives
screening score · 2026-10-24