The crucial question, therefore, is this: how can we guarantee

that AI decision-making is correct, safe, reliable and ethical?

For me, the answer is simple: we must apply our human

experience. As parents, we understand that it is our duty to

educate our children on what is right and wrong-to reinforce

societal norms, values and behaviors so that the next

generation is equipped to function as productive members of

society. The same approach needs to be taken with AI. What

is needed is a robust framework for teaching and training AI

applications-a machine learning curriculum that will help

ensure AI applications are aligned with our human values. Key

to this will be building a test framework, bespoke to AI

applications, to guarantee decision-making is transparent,

explainable, fair and non-discriminatory.

TESTING TIMES

Let us be clear: building such a test regime will be no easy feat. AI

applications comprise a large number of components, many of which

change over time. They are also fueled by vast amounts of structured

and unstructured data and processing this data can be challenging. The

algorithms themselves also need to come under scrutiny: software

engineers must carefully evaluate the accuracy and performance of the

learning models to ensure ethical and unbiased decisioning and

regulatory compliance. Engineers also need to build new test and

monitoring processes that account for the data-dependent nature of AI

systems. In the face of this complexity sits a clear requirement to

simplify design and validation processes to make the development of

ethical AI applications easier.