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2019/SOM1/EC/018 Agenda Item: 10d.ii

Singapore’s Artificial Intelligence Governance

Framework

Purpose: Information Submitted by: Singapore

First Economic Committee Meeting Santiago, Chile 4-5 March 2019

SINGAPORE’S AI

GOVERNANCE

FRAMEWORK

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SINGAPORE’S APPROACH TO AI

• Singapore as the heterogenous hub of hyperscale computing (HPC) globally

• Hub for Data-driven activities to facilitate access to datasets within and beyond Singapore

• Trusted environment with progressive regulations that balance between protection and innovation

• Good international AI companies anchored in SG

• Hi-value homegrown AI

companies

• Globally-recognized R&D capabilities in

niche areas of strengths

• Regional hub for

competitive AI talent equipped with critical

skillsets to address AI

opportunities

VIBRANT AI ECOSYSTEM WITH GLOBALLY-RECOGNISED CAPABILITIES

MOST AI-ENABLED ECONOMY

Sectors with higher risks of disruption

All businesses

Frontier AIOutpace competitiveness

through innovative AI use

SUPPORTED BY

Key sectors with strategic

economic impact and global standing

Addressing Disruption due to AITransform sectors and jobs to

cognitive, higher-VA activities

Leveling up AI use across economyEnsure sectors are up-to-date in AI

adoption

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SUPPORTING AI DEVELOPMENT AND ADOPTION THROUGH GOVERNANCE AND ETHICS

Funding research to identify and create solutions for legal,

regulatory and policy issues as AI adoption broadens

Bringing relevant stakeholders together to build a trusted ecosystem(such as Government, Industry, Consumers, and Academia)

Supporting AI adoption through model governance frameworks

that promote responsible AI and data use for voluntary adoption by corporates

1

2

3

ADVISORY COUNCIL ON ETHICAL USE OF AI AND DATA

Provide guidance on complex ethical issues arising from new business models and innovations in the AI space

Bring together AI technology providers, businesses that use AI and representatives of consumer interests

Host conversations with industry and consumers

Serves as an effective barometer of business needs and ground sentiments to shape the Government’s plan for a sustainable AI ecosystem

ADVISORY COUNCIL’S FOCUS AREAS

AI & Industry

Develop Model AI Governance Frameworks (cross-sector, sector-specific)

Promote model AI governance framework locally for adoption and internationally for thought leadership

Through key stakeholders: WEF, CIPL, Advisory Council members

AI & Society

Demystify & promote confidence in using AI

Audience-specific programmes and messaging:

- Students and youth

- Businesses/organisations

- Workers

Implement programmes through local & international partners

AI & Research

Develop industry-relevant AI governance research and promote international thought leadership on AI issues

Industry-relevant research through organisation sponsorship of problem statements. E.g.:

- Autonomous vehicles liability framework

- Fintech policy

- Cross-border data transfer issues

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AI & SOCIETY: TARGETED PROGRAMMES TO INSPIRE CONFIDENCE

Promote Use: ICM Learning Roadmap for students

• Real-life case studies for each field

• Understand advisory guidelines on the ethical

use of AI and data

• Know the code of practice for AI devt

Deeper understanding of AI:

• Discerning what is inside AI

• How AI works – Algorithms

• Real-life applications and value

• What is AI

• What can it do

• Debunk myths of AI

• Limitations of AI

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PHASE 1 FY 18/19

COMMUNITYORGANISATIONS

• Understand AI Governance

• Establish best practices to guide safe and ethical

management of AI systems

• Build consumer trust in AI deployments

• Prepare workforce (Training/Upskill/Re-skill)

Build

Trust &

Confidence

to USE

UNDERSTAND

AI and its benefits

KNOW

Awareness of AI

Basic knowledge and concepts of AI

De-Mystify AI

• Disruption of AI

• Business Transformation

driven by AI

Deeper understanding of AI:

• Relevant success use cases

• Importance of data

• Implications of AI at workplace

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5-YEAR RESEARCH PROGRAMME TO DEVELOP THOUGHT LEADERSHIP

Centre for AI & Data Governance in Singapore Management University’s School of LAW

Build up body of knowledge of the legal, policy and governance issues concerning AI and data us

Develop a pool of experts knowledgeable in these issues

Complement scientific AI research and professional training to build a robust AI ecosystem

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POTENTIAL TOPICS OF RESEARCH

AI & Society

• Trustworthy AI – ethical & social dimensions of developing trust in AI

• Privacy & data protection – legal, ethical and social dimension of using big data

• Transforming labour force

AI & Industry

• Legal foundations in the regulation of AI and data use

• Automated and connected vehicles policy

• Dispute resolution: opportunities & challenges AI presents to the industry

AI & Commercialisation

• Intellectual property and AI

• Data-logistics, AI and Transnational Commerce and Trading

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A BALANCED GOVERNANCE FRAMEWORK TO ENGENDER TRUST & ENABLE INNOVATIONPROMOTE RESPONSIBLE USE OF AI, ADDRESS ETHICAL RISK, AND BUILD CONSUMER TRUST

1.

2.

3.

Integrating AI ethics into corporate governance and risk

management structures e.g. corporate values, risk management frameworks,

decision-making and risk assessment

Translating responsible AI from principles into processes e.g. data

curation, addressing data bias, responsibilities in AI model selection, unintended discrimination,

model tuning

Establishing good consumer interactions e.g. AI-human interactions,

managing customer-relations when automating decision-making, explaining decision-making

process

Accountability-based framework – ready to use tool for orgs deploy AI in a responsible manner

www.pdpc.gov.sg/model-ai-gov

Model Framework Available From

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GUIDING PRINCIPLES FOR RESPONSIBLE AI

•Corporate governance structures, decision making models, oversight mechanisms, monitoring & reporting systems, periodic review

Internal Governance Structures & Measures

•Risk and impact assessments incorporating ethical considerations

•Degree of human intervention in AI decision-making

•Periodic review

Risk Management in AI Decision-Making

•Data curation, model formulation, model selection, training and tuning as phases

•Seeks to ensure quality, source, veracity, while mitigating inherent bias

Operations Management

•Build and maintain open relationship with customer that builds trust

•Feedback channels

Customer Relationship Management

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APPLYING MODEL AI GOVERNANCE FRAMEWORK

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