By: Chor-Ching Fan
AI governance programs are on the rise as the technology begins to transform the way organizations operate and treat their customers. While the best intentions drive growth strategy, executing it with newfound autonomous capabilities comes with a distinct set risks and unintended impacts that organizations need to understand and proactively mitigate. Getting ahead of AI-created business problems can start with a clear set of principles and ethical guardrails that guide responsible use of AI. While AI standards such as ISO 42001 AI and EU’s AI Act are valuable frameworks for AI compliance, most organizations will find it more practical to begin with a more targeted governance scope. Rizkly lets organizations begin with tailored a set of AI guidelines/controls/principles so the technology flourishes while meeting the expectation of relevant internal and external stakeholders today and tomorrow. With Rizkly, customers can also leap right into a more rigorous AI compliance effort using ISO 42001 AI or AI Act along with our quickstart guidance, policies, procedures and evidence samples. Another option is starting with a general set of AI principles that evolve as the organization learns and applies AI to different business processes. This approach allows for implementation of AI governance in the areas, and with the right methods, that matter most.
1. Accurate & Reliable – develop AI systems to achieve industry-leading levels of accuracy and reliability, ensuring outputs are trustworthy and dependable
2. Accountable & Transparent – establish clear oversight by individuals over the full AI lifecycle, providing transparency into development and use of AI systems and how decisions are made
3. Fair & Human-Centric – design AI systems with human oversight and diverse perspectives, and aligned with the organization’s values to mitigate risks of unfair discrimination and harmful bias
4. Safe & Ethical – prioritize the safety of human life, health, property, and the environment when designing, developing, and deploying AI systems
5. Secure & Resilient – mitigate potential cyber threats and vulnerabilities to ensure the robustness and resilience of AI systems
6. Interpretable & Documented – design AI systems to be interpretable, allowing humans to understand their operations and the meaning and limitation of their outputs. Document design decisions, development protocols, and alignment with responsible AI principles
7. Data Privacy & Security – develop AI systems with careful attention to privacy, security, confidentiality, and intellectual property ownership considerations around the data used
8. Vendor Management – exercise diligence and ongoing oversight when selecting third-party vendors involved in AI system development (eg, data brokers, cloud service providers)
9. Continuous Monitoring – establish standards for continuous monitoring and evaluation of AI systems to uphold ethical, legal, and social standards and performance benchmarks
10. Continuous Learning – commit to continuous learning and development of AI systems through adaptive training, feedback loops, user education, and regular compliance auditing to remain aligned with ethical, legal, and societal standards
If some or all of these AI principles sound like a good starting point for beginning your AI governance and/or compliance efforts, please contact us…we’d love to show you why organizations prefer our compliance automation platform for achieving and sustaining AI governance efforts effectively and efficiently.
