How do I balance accuracy and accountability with AI risk models?

The AI now models risk in ways you cannot always interpret, and you are caught in a real tension. The model may be more accurate, yet you are the one who must answer for it, and you cannot fully account for what you cannot understand. That tension is not a flaw in you. It is one of the central problems of your field right now. There is no clean answer, but there is a principle. Accuracy without accountability is fragile, because the day something goes wrong, someone must explain why, and no result is worth being unable to stand behind it. Your job has always been not just to find the number but to be answerable for it, and that duty does not disappear because the tool got cleverer. So weigh both, and where you cannot explain a model, be honest about that limit rather than hiding behind its precision. Push for tools and methods that let you see inside. And keep asserting that being accountable is part of the work, not an obstacle to it. The profession needs people who hold that line, and you are positioned to be one.