Risk Tiering an AI Use-Case

When looking at the risk involved with procuring or developing an AI system, it isn’t enough to examine the risk generated by the AI model itself. AI models exist within systems. Systems exist within organizations. And organizations exist within a context. 

Starting with a context, there are a number of factors that can affect the level of risk faced by a company that is developing, deploying, or using AI. For instance, ‘healthcare’ as a sector is often seen as being “high-risk” but an AI-driven scheduling system for physicians is less risky than an application for AI-driven diagnostics. This shows that the use-case within a sector affects risk. 

Furthermore, the type of AI technology a supplier uses affects risk as well. Some systems don’t even use AI, but rather simpler models that could be run out of an Excel file. In those cases, it's very easy to trace how an input turns into an output, so risk is lower when compared to an AI model that’s probabilistic and may have slightly different outputs every time it's run.

From another perspective, a company might be developing an AI system but hasn’t released it yet. As a result, that company wouldn’t have any users and no users means less risk than a system that was in use. 

An additional factor when considering the context an AI-driven organization operates in is its legal jurisdiction. Some jurisdictions have passed laws regarding AI use (such as the EU AI Act in Europe) whereas other jurisdictions rely on applying existing laws to AI use cases.

When combining all of these factors, a company procuring or developing AI technology can understand the level of risk they’re taking on by operating a certain kind of model for a certain kind of use in a certain type of sector. 

Fairly's award-winning platform can help ease your team's minds when trying to understand risk, book a call with us to learn more.

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