Why robustness is key to deploying AI

First, ML will struggle most in adversarial situations, where other agents are incentivized to subvert the model. On the military side, this includes cyberdefense, intelligence collection, and any use of ML on the battlefield. On the civilian side, it applies to detecting fraud, human trafficking, poaching, or other illegal behaviors. In such settings, we must also assess the costs of failure and speed of turnaround. In a military setting, even temporary failure of an ML system can be catastrophic, while poachers who temporarily evade detection may still be eventually caught.