Quantifying the risks of AI
If AI is to reach its full potential, organizations need the ability to predict and measure conditions that amplify risks and undermine trust.
Understanding the drivers of risk in relation to AI requires consideration across a wide spectrum of contributing factors including its technical design, stakeholder impact and control maturity. Each one of these, in their design and operation, can affect the risk level of an AI system. Developing an understanding of the risk drivers for an AI system is a complex undertaking. It requires careful consideration of potential stakeholder impacts across the full lifecycle of the AI system.
In developing a trusted AI platform, there are three important components to managing the risks of an AI system:
- Technical risk — It evaluates the underlying technologies, technical operating environment and level of autonomy.
- Stakeholder impact — It considers the goals and objectives of the AI agent and the financial, emotional and physical impact to external and internal users, as well as reputational, regulatory and legal risk.
- Control effectiveness — It considers the existence and operating effectiveness of controls to mitigate the risks of AI.
Together, these provide an integrated approach to evaluate, quantify and monitor the impact and trustworthiness of AI. A trusted AI platform uses interactive, web-based schematic and assessment tools to build the risk profile of an AI system, and then an advanced analytical model to convert the user responses to a composite score comprising technical risk, stakeholder impact and control effectiveness of an AI system.
This kind of platform can be leveraged by organizations to develop a risk quantification during a robust desk-top design and challenge function at the beginning of their AI project. Embedding trust requirements in the design of AI systems from the outset will result in more efficient AI training and higher user trust and adoption.