Table 1 Compilation of fundamental ethical principles (IMDA &PDPC, 2020)

1. Accountability: Ensure AI actors are held responsible for the AI systems’ functioning and adherence to ethical principles

2. Accuracy: Recognize and communicate sources of error and uncertainty in algorithms and data to inform mitigation procedures

3. Auditability: Allow third parties to examine and review algorithm behavior through transparent information disclosure

4. Explainability: Ensure that algorithmic decisions and underlying data can be explained in layman’s terms

5. Fairness: Prevent discriminatory impacts, include monitoring mechanisms, and consult diverse perspectives during system development

6. Human Centricity and Well-being: Prioritize the well-being and needs of humans in AI development and implementation

7. Human rights alignment: Ensure technologies do not violate internationally recognized human rights

8. Inclusivity: Make AI accessible to everyone

9. Progressiveness: Favour projects with significantly greater value than their alternatives

10. Responsibility, accountability, and transparency: Build trust through responsibility, accountability, and fairness, provide avenues for redress, and maintain records of design processes

11. Robustness and Security: Ensure AI systems are safe, secure, and resistant to tampering or data compromise

12. Sustainability: Favour implementations that provide long-lasting, beneficial insights and can predict future behavior