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.
A new framework for building AI programs suggests a way to prevent aberrant behavior in machine learning by specifying guardrails in the code from the outset. It aims to be particularly useful for nonexperts deploying AI, an increasingly common issue as the technology moves out of research labs and into the real world.
The approach is one of several proposed in recent years for curbing the worst tendencies of AI programs. Such safeguards could prove vital as AI is used in more critical situations, and as people become suspicious of AI systems that perpetuate bias or cause accidents.