Record STMT-M050Voluntary

Model Artificial Intelligence Governance Framework

MODEL ARTIFICIAL INTELLIGENCE GOVERNANCE FRAMEWORK

Framework Institutional policySouth-eastern Asia · SGadopted

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

Personal Data Protection CommissionRegulator · SG

Lead issuer

Info-communications Media Development AuthorityRegulator · SG

Co-issuer

Legal forceVoluntary

Under whose authorityInstitutional policy

Judged fromabstract

Who it governs

CompaniesPublic sector bodiesAI developers and providersAI deployers and usersResearchers and academics

Organizations and enterprises deploying AI solutions at scale who wish to implement responsible and accountable AI governance practices.

Sectors

Cross-sector

What the document contains

What's covered

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.

What's missing

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.

Key takeaways

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.

Principles it states

Organisations using AI in decision-making should ensure that the decision-making process is explainable, transparent and fair.AI solutions should be human-centric.

Outline

· Summary of Updates
· Foreword
· 1. Preamble
· 2. Introduction
· 3. Model AI Governance Framework

Issued21 January 2020

Length2,727 words · 70 pages

FormatNot recorded

Scopenational

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

4/5

Clear 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

4/5

Non-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

0/5

Respect for the inherent worth of persons

Introduction

screening score · 2026-10-24

Human Oversight

3/5

Humans 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

3/5

Protection 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

4/5

Openness 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

0/5

Consequences and compliance mechanisms with real force

Introduction

screening score · 2026-10-24

Impact Assessment

4/5

Required 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

Topics

ai_governance ethical_ai accountability risk_management data_protection