This phrase refers to the fact that many AI systems—especially machine learning models—are so complex that it’s very difficult to see how they reach their conclusions. Inputs go in (like data), and outputs come out (like predictions or classifications), but the decision-making process inside is often opaque, even to the people who built the system. This lack of transparency makes it hard to identify bias, explain errors, or hold designers accountable, and it can reinforce the illusion that AI is neutral or objective when in reality it reflects human assumptions and societal inequities.