A Pathway To AI Governance

Effective AI governance is a tremendous challenge in itself. Implementing it across different sectors that require navigation of diverse regulatory landscapes, organizational priorities, and rapid AI developments makes it even more challenging. Furthermore, any AI systems and models being deployed need to be transparent, accountable, and fair. Since there is a lack of standardized tools and metrics to measure compliance, organizations must determine whether they meet the ethical and regulatory requirements. 

AI governance eliminates all risks associated with bias and discrimination in AI systems by ensuring the AI models in use are trained on a diverse and representative dataset that is regularly audited and assessed for fairness. This ensures that the outputs generated by such models are both fair and free from biases. Furthermore, organizations may deploy bias detection tools and various accountability mechanisms to continuously monitor and adjust their AI systems’ operations. 

The difference between the three is in the degree of oversight. Informal AI governance results in minimal oversight, where ethical considerations are acknowledged but not implemented. Ad hoc governance is more reactive in nature, where policies, processes, and mechanisms are adopted, but only in case an issue arises. Lastly, formal AI governance is a comprehensive AI strategy about how an organization aims to ensure its AI-related practices comply with regulatory and ethical standards via regular assessments.