In regards to your comment on AI safety being "underutilized", my thoughts are that it's just simply difficult to do. Let's put aside all the difficulties of training, verification, etc and just look at the data problem.
If you wish to make certain that your system meets some given AI safety standard, then you must somehow prove two things: the data the model ingests when deployed will always return the correct response and that the dataset composes/generalizes the data the model will ingest when deployed. For simple problems, this may be doable. For complex, multidimensional problems wherein the dataset must only hope to generalize the complex input it will encounter during deployment, this may be next to infeasible.
I'm definitely getting off topic here, but bias of all kinds exists even human operated systems eg car. I can't say I've ever seen a firetruck stopped on the highway before, but perhaps I'd know what it is and how to avoid it. If a dataset does not contain that event, how can we be certain an AI system would understand? I'm not sure if it's possible to create a dataset that will be without bias in the case of complex problems, but I'm certain we can create one that's performant at driving than I. So the questions of "how safe is enough?", then proving/demonstrating that safety, and more are particularly open topics. I enjoy making the point that there is the lack of rigorous standards for humans, as we hold computers to far higher standards, but ML models probabilistically navigate decisions similar to us.
I'm sure this reply could extend further, but this and more are my defense of why I believe AI safety is a wide topic. None of the above should dissuade beginners from exploring the subtopic, but it's certainly not something you'd be able to learn first without strong, foundational context.