Advai: Streamlining AI Governance with Advai Insight for Enhanced Robustness, Risk Management and Compliance

Advai Insight is a platform for enterprises that transform complex AI risks and robustness metrics into digestible, actionable insights for non-technical stakeholders. It bridges the communication divide between data science experts and decision-makers, ensuring that the management of AI risk and AI regulation compliance, is efficient and informed.
This approach was taken to empower senior stakeholders with the necessary insights to make informed decisions about AI applications without requiring them to have a technical background. Thus, governance and compliance are enhanced and risk is managed more effectively.

This creates a positive knowledge-action feedback loop. Senior stakeholders are enabled to provide technical teams with directives in the language of business objectives and monitor the technical progress towards these objectives in equivalent terms. 

Facilitates informed decision-making at the senior level, improving governance and strategic planning.

• Provides a ‘full picture’ of an organisation’s portfolio of AI models.
• Enhances understanding and oversight of AI risks and performance across the entire AI estate.
• Streamlines the compliance process, aiding in lawful and ethical AI deployment.
• Aids in efficient resource management by enabling organisations to schedule resources for AI model retraining, deployment, analysis, etc. against the priority and risk level of each model. In other words, a full picture helps managers consider the big picture and prioritise where resource is needed.

It may not fully replace the need for technical expertise in interpreting and acting on AI risks and compliance issues. At least, the coordination of risk, compliance and engineering resources falls to the organisation.

• Dashboard insights are only as good as their connectedness to the organisation’s portfolio of algorithms. The organisation must ensure new models across the organisation are connected and old models are disconnected to preserve the accuracy of the dashboard.
• Understanding nuanced aspects of AI performance may still require detailed explorations by technical experts whom would provide explanations beyond the dashboard’s capabilities.
   

Related links: 

• Link to the full use case.

This case study was published in collaboration with the UK Department for Science, Innovation and Technology Portfolio of AI Assurance Techniques. You can read more about the Portfolio and how you can upload your own use case here.