Artificial intelligence has given us algorithms capable of recognizing faces, diagnosing disease, and, of course, crushing computer games. But even the smartest algorithms can sometimes behave in unexpected and unwanted ways, for example picking up gender bias from the text or images they are fed. This talk discusses a framework to prevent aberrant behavior in machine learning by specifying guardrails in the code from the outset. It aims to be particularly useful for non-experts deploying AI, an increasingly common issue as the technology moves out of research labs and into the real world.