How do I catch bias and errors in AI research?

You use AI for research and drafting now, and it speeds things up, but you worry about bias and the errors you cannot see. In policy work that worry matters, because what you write can shape decisions that touch a lot of lives. The honest picture is that these tools are confident whether they are right or wrong. They can invent a source, smuggle in a slant, or miss what does not appear in their training. None of that announces itself. So the errors you cannot see are exactly the ones to plan for, by never treating the output as finished truth. So build one verifying step you never skip. For anything that will actually go somewhere, check the key claims and sources yourself, against the real thing, before it leaves your hands. This week, do that once, deliberately. The tool can draft fast. Whether it is right is still your call, and that is where your value sits.