AI GOVERNANCE IN MALAYSIA RISKS, CHALLENGES AND PATHWAYS FORWARD:

A critical review by Strasys Solutions Malaysia

Dr. Suresh Kumar Krishnan

Strasys Solutions Malaysia

Feb 2025

 

The Khazanah Research Institute's report, AI Governance in Malaysia: Risks, Challenges and Pathways Forward (2025), analyzes Malaysia’s AI governance landscape, focusing on risks, challenges, and policy recommendations. This is definitely a timely effort by the KRI in highlighting the immediate actions needed to ensure ethical and responsible AI in Malaysia with a holistic approach to AI Governance (AIG). While the beginning part of the document very clearly explains the current scenarios in various circumstances in Malaysia especially the risks and challenges, however we believe that the recommendations need to be further investigated for possible overlooked aspects and missed opportunities. Based on the recommendations provided and the broader context of AI governance, here are some areas that the report could have further emphasized or explored:

1. Ethical AI Integration: Explicit strategies to embed ethical AI principles into public-sector procurement and education curricula.
2. Local AI Ecosystem Development: Incentives for homegrown AI solutions to reduce foreign dependency and ensure cultural relevance. This is what Strasys Solutions Malaysia have been always advocating for that is creating AI Entrepreneurs among Malaysians.
3. Environmental Sustainability: Policies to mitigate AI’s carbon footprint (e.g., energy-efficient data centers, green AI standards).
4. Accessibility and Inclusivity: Ensuring that AI benefits all segments of society, including marginalized communities and people with disabilities. This requires addressing issues such as data bias, algorithmic fairness, and digital literacy. The report could have included specific recommendations to promote AI accessibility and inclusivity.
5. Public Engagement and Trust: While the report mentions public awareness, it could emphasize the importance of ongoing public engagement and dialogue to build trust in AI systems. This includes involving citizens in the development of AI policies and regulations.
6. Specific Industry Focus: The report provides general recommendations. However, AI impacts and governance needs vary significantly across sectors (e.g., healthcare, finance, manufacturing). A deeper dive into specific industry needs and challenges would make the recommendations more actionable.
7. Incentivizing Ethical AI Development: While the report touches on regulation, it could explore incentives for companies and researchers to prioritize ethical AI development (e.g., tax breaks for companies that adopt AI ethics frameworks, funding for research on AI safety).
8. Addressing Job Displacement: The report mentions the risks of not adopting AI, but it does not fully address the potential for job displacement due to AI-driven automation. Recommendations could include retraining programs, social safety nets, and exploring new economic models.
9. Metrics for Success: The recommendations are somewhat broad. The report could have suggested specific, measurable, achievable, relevant, and time-bound (SMART) metrics to track the progress of AI governance in Malaysia. This would allow for better evaluation and adjustment of policies over time.

The interesting point we would like to bring forward is about a section in the report that that talks about “4.5 Issues beyond Malaysia’s control”. There must be an analysis of controllability and alternative methods & mechanisms to address these matters. While the report frames these issues as being "beyond Malaysia's control," a more nuanced perspective reveals that Malaysia can exert influence and implement strategies to mitigate the associated risks and challenges.

1. Dependence on Foreign APIs, Datasets, and Technological Components:

Framing: While Malaysia cannot directly control the policies of other countries, it can reduce its dependence and associated risks.

• Alternative Approaches:
• Invest in Local AI Infrastructure: Develop domestic cloud services, machine learning frameworks, and open-source initiatives to reduce reliance on foreign providers.
• Promote Local AI Development: Encourage local AI startups and research institutions to create alternative technologies and datasets tailored to the Malaysian context.
• Diversify Supply Chains: Explore partnerships with multiple international providers to avoid over-reliance on a single source.
• Establish Clear Procurement Standards: Implement standards for government procurement of AI solutions, favoring vendors that adhere to ethical guidelines and data security standards.
1. Cross-Border Data Flow and Differing Privacy Standards:

Framing: While Malaysia cannot force other countries to adopt its privacy standards, it can implement robust data governance frameworks and international collaborations.

• Alternative Approaches:
• Strengthen National Data Protection Laws: Enhance the Personal Data Protection Act (PDPA) to align with international best practices, such as the GDPR.
• Negotiate Data Transfer Agreements: Establish bilateral or multilateral agreements with countries that have compatible data protection standards to facilitate secure data transfers.
• Implement Data Localization Policies: Consider data localization requirements for sensitive data to ensure it remains within Malaysian jurisdiction.
• Promote Privacy-Enhancing Technologies: Invest in and promote the use of technologies like anonymization, pseudonymization, and differential privacy to protect data during cross-border transfers.
2. Lack of Shared Mental Models:

Framing: While achieving complete consensus on AI concepts is challenging, Malaysia can foster greater understanding and alignment among stakeholders.

• Alternative Approaches:
• Develop National AI Terminology Standards: Create a clear and consistent glossary of AI terms and concepts for use in policy, regulation, and public discourse.
• Promote Cross-Disciplinary Collaboration: Facilitate communication and collaboration between policymakers, developers, researchers, ethicists, and the public through workshops, conferences, and advisory boards.
• Invest in AI Education and Awareness: Launch public awareness campaigns and educational programs to improve understanding of AI concepts and their implications.
• Establish Ethical Guidelines and Frameworks: Develop ethical guidelines and frameworks for AI development and deployment, providing a common reference point for stakeholders.

While some factors related to AI governance may seem beyond Malaysia's direct control, we at Strasys Solutions Malaysia believe that Malaysia has options to mitigate risks and shape the AI landscape. Proactive measures, strategic investments, international collaborations, and robust regulatory frameworks can empower Malaysia to navigate challenges and harness the benefits of AI in a responsible and sustainable manner. The key is to shift from passive acceptance of external factors to an active approach of shaping the domestic AI ecosystem and engaging strategically with the international community.

We would like to raise another critical point about the potential limitations in the report's focus and its implications for Malaysia's AI strategy. The statement from the report that Malaysia should focus more on governing the use of AI rather than the development of AI models reflects a potentially limiting perspective.

“Second, our research leads us to conclude that Malaysia should focus more on governing AI use and deployment and less on governing the development of AI models. As Malaysia is not primarily a developer of models at this time, AI risks for the nation have more to do with how and how much AI systems are used than how they are built and trained. While recognizing the need for AI development standards to be met, the country’s governance priorities should address risks more relevant to it.”

Why do we say this?

• Missed Opportunities in AI Solution Development: By prioritizing governance of AI use, the report seemingly downplays the potential for Malaysian companies and researchers to develop innovative AI-based solutions tailored to local needs and contexts. This could stifle the growth of a domestic AI industry and limit Malaysia's ability to compete in the global AI market.
• Overly Simplistic View of AI as a Commodity: The report's stance appears to treat AI as a commodity that Malaysia primarily consumes, rather than a technology that it can actively shape and contribute to. This neglects the potential for Malaysia to become a creator and exporter of AI solutions.
• Dependence on Foreign AI Technologies: By focusing solely on regulating the use of foreign AI models, Malaysia risks becoming overly dependent on external technologies and potentially vulnerable to geopolitical or economic shifts.
• Innovation and Adaptation: The report overlooks the importance of adapting and fine-tuning existing AI models to address specific Malaysian challenges and opportunities. This requires a certain level of AI development expertise and capacity.
• Strategic Control: Expertise in AI model development provides strategic control over technology. Relying solely on foreign models means relinquishing control over key aspects of the AI value chain.

Strasys recommendation for Policy: Instead of solely focusing on governing AI use, a more strategic approach for Malaysia would be to pursue a dual-track strategy that balances responsible AI adoption with fostering domestic AI innovation and development. This approach should be:

1. Promote AI Development and Adaptation:
• Invest in AI Research and Education: Increase funding for AI research at universities and research institutions. Establish scholarships and training programs to develop a skilled AI workforce.
• Support AI Startups and Innovation: Provide funding, mentorship, and infrastructure to support AI startups and entrepreneurs. Create incubators and accelerators focused on AI.
• Encourage Data Accessibility: Promote the responsible sharing and use of data for AI development, while ensuring data privacy and security. Develop national data standards and platforms.
• Foster Collaboration: Encourage collaboration between industry, academia, and government to drive AI innovation. Establish industry-led consortia focused on specific AI applications.
2. Implement Risk-Based AI Governance:
• Develop a National AI Ethics Framework: Create a clear and comprehensive ethics framework to guide the responsible development and deployment of AI.
• Establish Sector-Specific Regulations: Develop regulations tailored to the specific risks and opportunities of different sectors (e.g., healthcare, finance, transportation).
• Promote Transparency and Accountability: Require transparency in AI systems and establish mechanisms for accountability in case of harm.
• Invest in AI Auditing and Testing: Develop capabilities for auditing and testing AI systems to ensure they are safe, reliable, and unbiased. Develop Certified AI Governance Personnel recognized in governing AI solutions (AI Governance (AIG) Audit Training Program powered by the DATARUSH® Framework).
3. Enhance International Collaboration:
• Engage in Global AI Governance Discussions: Actively participate in international forums to shape global AI standards and norms.
• Forge Partnerships: Collaborate with other countries on AI research, development, and governance.
• Promote Cross-Border Data Flows: Establish agreements and frameworks to facilitate secure and responsible cross-border data flows.

In conclusion, by adopting this dual-track approach, we at Strasys Solutions strongly believe that Malaysia can not only mitigate the risks of AI but also harness its transformative potential to drive economic growth, improve public services, and enhance the well-being of its citizens. The government should also prioritize the development of local talent and expertise in AI, rather than simply relying on foreign technologies. This will enable Malaysia to become a leader in the region's AI landscape and ensure that its AI policies are tailored to its specific needs and circumstances.