Robust And Secure Ai: A Primer For The C-suite

So you’ve invested in artificial intelligence (AI). The first questions your board may ask will likely be related to what it can do, how will it improve business processes, save money or provide greater experiences for customers. However to be responsible there are two questions that should be asked first:

WILL IT PERFORM AS INTENDED AT ALL TIMES? IS IT SAFE?

When we speak of robustness in AI, we’re talking about its performance ability. Can the system perform consistently under dynamic circumstances? Security, on the other hand, is about the safety of the technology itself, whether it is protected from abuse or appropriation.

These aren’t just technical challenges for data scientists; they’re business problems. As such, any business strategy around the use of AI needs to include the testing of its robustness and security — before deployment. Doing so will allow potential weaknesses to be identified and decisions around any required mitigation or remediation to be taken to executive leadership. Solutions may demand compromise, perhaps even of a desired attribute of the system itself.

For example, a complex AI model might do a great job of predicting the probability of approval of a credit limit increase for a large proportion of customers. This is a useful tool for targeting offers and business development opportunities. But if the algorithm does less well for some groups of customers, it may lead to poor business outcomes.The C-suite needs to have oversight of the robustness of the AI to ensure that it is not only performing to the financial benefit of the organisation, but upholding company values as well.

While data scientists can, and should, identify and flag these trade-offs, it is the C-Suite and related stakeholders who are ultimately accountable to customers and regulators — and who should balance AI decisions in light of broader business strategy.