What is "best practice" when working with AI in the real world?

Working with AI in real world conditions can be quite a different proposal to the idealised settings often discussed "in theory". Guest editor Anna Demming speaks to a panel of experts about how to meet "best practice" aspirations within real world constraints, and how to avoid common pitfalls. Over the course of the Real World Data Science AI series, we've had articles laying out the nitty gritty of what AI is, how it works, or at least how to get an explanation for its output as well as burning issues around the data involved, evaluating these models, ethical considerations, and gauging societal impacts such as changes in workforce demands. The ideas in these articles give a firm footing for establishing what best practice with AI models should look like but there is often a divide between theory and practice, and the same pitfalls can trip people up again and again. Here we discuss how to wrestle with real world limitations and flag these common hazards. It is often said that while almost everybody is now trying to leverage AI in their projects, most AI projects fail. What nuggets of wisdom do the panel have for swelling that minority that succeed with their AI projects, and what should you do before you start doing anything? Ali Al-Sherbaz: It's not easy to start, especially for people who are not aware how AI works. My advice is, first, they have to understand the basics of how AI works because the expectation could be overpromising, and that is a danger. Just 25 years ago, a master dissertation might be about developing a simple – we call it simple now but it was a master's project 25 years ago – a simple model with a neural network of a combination of nodes to classify data. Whatever the data is – it could be drawing shapes, simple shapes, square, circle triangle – just classifying them was worth an MSc.