The Complexity of Artificial Intelligence

The discourse surrounding artificial intelligence (AI) has become increasingly complex as this rapidly advancing technology continues to transform industries, influence societal norms, and raise profound ethical questions. As AI algorithms are applied across multiple domains, there are growing calls to ensure these systems are transparent, accountable, and free of bias.

Questions have also been raised over the blurring of boundaries between humans and machines, as well as the ethical implications of machines with unbridled decision-making capabilities.

The Role of Causal AI

As Hong Kong explores AI governance, the inherent limitations and risks of current AI technologies underscore the growing importance of the emerging field of causal AI, also known as causal reasoning or modelling AI.

Unlike traditional machine learning approaches that focus on identifying statistical patterns, causal AI focuses on understanding the causal relationships between variables in a system, rather than associations or correlations, which are prone to data inaccuracies.

Importance of Explainability and Accountability

The field of causal AI is poised to play an increasingly important role in developing more trustworthy and valuable systems, and has shown impressive capabilities across domains. The methodology holds significant promise for high-stakes applications, such as in healthcare, finance, and public policy, where “explainability” and accountability are paramount.

However, causal AI represents a paradigm shift in the human-AI dynamic and can help address the significant concern that increasingly, AI systems will have limited or no human interaction.

Regulatory Frameworks and Global Cooperation

Implementing the EU Artificial Intelligence Act sets the stage for increased international cooperation and harmonisation of governance standards around AI. Like climate change, AI requires coordinated efforts and collaboration among stakeholders across different jurisdictions to ensure responsible development and implementation.

Like many other jurisdictions, Hong Kong lacks a comprehensive statutory or regulatory framework to govern AI. There are guidelines on the ethical use of AI and the Monetary Authority has issued “high-level principles”, recognising the need to provide guardrails and guidance. But much more robust regulatory oversight is required.

Cybersecurity Considerations

Within this context, cybersecurity remains a key consideration. Traditional machine learning models, often reliant on statistical correlations in data, can be vulnerable to sophisticated cyberattacks that exploit these patterns. In contrast, causal AI’s focus on uncovering causal mechanisms offers a more robust defence against malicious actors seeking to manipulate or subvert AI systems.

By modelling the causal drivers of complex phenomena, causal AI can better detect anomalies, identify cyber threat sources, and respond with greater agility. This capability is essential as AI increasingly integrates into critical infrastructure, financial systems, and other high-stakes domains.

Conclusion

By seizing the opportunity to establish a comprehensive, evidence-based approach to AI governance and regulation, Hong Kong can become a significant global player in the ethical and responsible use of these powerful technologies.

Dr Jane Lee is president of Our Hong Kong Foundation

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