Record STMT-W27C7E6Voluntary

Model Artificial Intelligence Governance Framework

MODEL ARTIFICIAL INTELLIGENCE GOVERNANCE FRAMEWORK

Framework Institutional policySouth-eastern Asia · SGin force

The Model Artificial Intelligence Governance Framework (Second Edition) provides a voluntary, flexible guide for organizations deploying AI at scale to translate ethical principles into practical measures. It focuses on internal governance structures, determining the level of human involvement in AI-augmented decision-making, operations management, and stakeholder interaction and communication. The framework is designed to build stakeholder trust, promote human-centric AI, and complement Singapore's National AI Strategy.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Info-communications Media Development AuthorityRegulator · SG

Lead issuer

Personal Data Protection CommissionRegulator · SG

Co-issuer

Legal forceVoluntary

Under whose authorityInstitutional policy

Judged fromfull text

Who it governs

CompaniesRegulatorsPublic sector bodiesAI developers and providersAI deployers and users

Organizations and enterprises deploying artificial intelligence solutions at scale.

Sectors

Cross-sector

What the document contains

What's covered

The document addresses internal governance structures, methodologies for determining human involvement in AI decision-making (using a probability-severity of harm matrix), operations management including data quality and algorithm robustness, and stakeholder communication strategies.

What's missing

The framework does not address specific legal liabilities, intellectual property rights, societal employment impacts outside the governance scope, or mandatory legal compliance for sector-specific laws.

Key takeaways

The Model Framework provides voluntary, practical guidance for organizations to implement responsible AI practices aligned with ethical principles.
Organizations should establish internal governance structures with clear roles and risk management controls for AI deployment.
The degree of human oversight should be determined by weighing commercial objectives against the probability and severity of potential harm to individuals.
Operations management requires robust practices across data quality, data lineage, algorithm explainability, repeatability, robustness, and auditability.
Stakeholder interaction and communication strategies should emphasize transparency, general disclosures, explainability policies, and accessible feedback channels.

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, prioritizing the interests, well-being, and safety of human beings.

Outline

· Summary of Updates
· Foreword
· 1. Preamble
· 2. Introduction
· 3. Model AI Governance Framework
  - Internal Governance Structures and Measures
  - Determining the Level of Human Involvement in AI-augmented Decision-making
  - Operations Management
  - Stakeholder Interaction and Communication
· Annex A: For Reference: A Compilation of Existing AI Ethical Principles
· Annex B: Algorithm Audits
· Acknowledgements

Issued21 January 2020

Length16,297 words

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

Responsibility for and oversight of the various stages and activities involved in AI deployment should be allocated to the appropriate personnel and/or departments.

Section 3

screening score · 2026-10-23

Fairness

4/5

Non-discrimination and equitable treatment

Ensure that algorithmic decisions do not create discriminatory or unjust impacts across different demographic lines (e.g. race, sex, etc.).

Annex A

screening score · 2026-10-23

Human Dignity

3/5

Respect for the inherent worth of persons

Engage in data practices that encourage the practice of virtues that contribute to human flourishing, human dignity and human autonomy.

Annex A

screening score · 2026-10-23

Human Oversight

4/5

Humans remain in control of consequential decisions

Based on the risk management approach described above, the Model Framework identifies three broad approaches to classify the various degrees of human oversight in the decision-making process

Section 3

screening score · 2026-10-23

Transparency

4/5

Openness about how AI systems work and are used

Organisations are encouraged to provide general information on whether AI is used in their products and/or services.

Section 3

screening score · 2026-10-23

Enforcement

4/5

Consequences and compliance mechanisms with real force

Conducting an algorithm audit requires technical expertise which may require engaging external experts. The audit report may be beyond the understanding of most individuals and organisations.

Annex B

screening score · 2026-10-23

Impact Assessment

4/5

Required assessment of an AI system's risks/impacts before or during use

Mastercard conducts initial risk scoring to determine the risk of the proposed AI activity, which includes an evaluation of multiple factors including alignment with corporate initiatives, the data types and sources utilised, and the impact on individuals from AI decisions.

Section 3

screening score · 2026-10-23

Topics

artificial_intelligence governance_framework ethics accountability risk_management