How fitness functions can help us govern and measure AI

AI is inherently dynamic: that's true in terms of the field itself, and at a much lower level too — models are trained on new data and algorithms adapt and change to new circumstances and information. That's part of its power and what makes it so exciting, but from a business and organizational perspective, that can make governance and measurement exceptionally difficult. How can we know that our AI is optimized for the right thing? How can we be sure it's oriented towards what we want it to be? This is where the concept of fitness functions can help. Broadly speaking, fitness functions are ways of measuring the extent to which a given solution is fulfilling its goals — so, in the context of AI, they can help teams ensure that AI systems are serving their intended purpose. In this episode of the Technology Podcast, Rebecca Parsons and Neal Ford — authors (alongside Pat Kua and Pramod Sadalage) of Building Evolutionary Architectures, the book which brought fitness functions into the software architecture space — join host Ken Mugrage to explore how the fitness function concept can help us better manage the dynamism of AI and, in doing so, overcome the challenge of bringing such systems into production. Learn more about Building Evolutionary Architectures: https://www.thoughtworks.com/insights/books/building-evolutionaryarchitectures-second-edition