Practical Steps Towards Responsible Governance of AI-Enabled Systems

AI is advancing rapidly, and is extremely powerful at developing insights and correlations, even exceeding human capability. However, as humans we are struggling to understand how and what AI has learned, and how it can safely augment and in some cases far extend the potential of human decision-making.

Whilst extremely powerful, there is significant work to understand and govern the behaviour to achieve safe and responsible AI goals.

KJR Chief Technology Officer Dr Mark Pedersen and Founding Chairman Dr Kelvin Ross have collaborated to produce a thought-leading white paper on Responsible AI.
The white paper titled “Practical Steps Toward Responsible Governance of AI-Enabled Systems” provides several key learnings –

Technical and Procedural Compliance Requirements: Organisations deploying AI-enabled systems need to implement procedural compliance practices such as transparency, accountability, and fairness. This includes ensuring users and stakeholders understand how the AI functions and makes decisions, determining responsibility for AI decisions, and ensuring the AI does not perpetuate biases or discrimination.

It also involves risk evaluation, human oversight, and assessing the wider societal benefits of AI.

Governance Processes: Organisations deploying AI-enabled systems need to have formal governance processes in place to control the quality of their software deployments. These processes should align with the principles of responsible AI and any specific governance frameworks and legislation applicable to the local jurisdiction.

Unlike traditional software systems, AI-enabled systems require extensive evaluation, fine-tuning, and continual monitoring to maintain their performance quality.

Validation Driven Machine Learning (VDML): VDML is a methodology developed by KJR to guide organisations in deploying robust and reliable AI solutions. By clearly defining the benefits and context in which the AI-enabled system is being used, stakeholders can assess risk and set a baseline for evaluation.

Founded on over 26 years’ expertise in risk management and quality assurance, KJR’s VDML methodology guides customers in the selection of effective risk assessment, quality assurance and governance mechanisms for a specific operational context.

These key learnings highlight the importance of compliance, governance processes, and a systematic approach like VDML in promoting responsible governance of AI-enabled systems.

The document also spotlights SmartAIConnect’s Responsible AI (RAI) Framework which enables safe, rapid and secure deployment and governance of AI-enabled systems at an enterprise scale.