I feel your pain. I don’t think the issue is falling on deaf ears, rather that it is not easy to fix. Hopefully the changes to the new app will help the situation somewhat, but it won’t be a complete solution.
It seems useful to link back to the observation accuracy experiments done some time ago. These indeed find that fungi is the iconic taxon with the highest % of misidentifications on the site, at least when looking at Research Grade observations (Chromista and Protozoa are worse when including all observations). That said, in the sample selected for the experiment, 86% of Research Grade fungus observations were assessed as accurate. When looking at all observations, the sample shows only 72% of fungal observations assessed as correct. Better than 10%, but still far from ideal.
One thought this gives me is that it may be worthwhile, and more satisfying, for fungus identifiers to focus more on fixing incorrect Research Grade observations, rather than doing battle with the flood of incorrect Needs ID observations.
Other than that, I’m not seeing a clear, actionable solution to this issue that doesn’t come with major costs or downsides. One could have some system for manually designating taxa where the Computer Vision should never suggest anything finer than Genus, or even Family. For example, whenever the Computer Vision “thinks” an observation is Russula emetica it could just suggest Russula sp… but who decides that, and how? Would there be a voting system? Open to anyone, or just curators? How would that information be integrated into the way the Computer Vision works? What happens with taxon changes or when users disagree? And so on. It wouldn’t be a trivial change to implement, but I agree that something like this is needed, and the details need to be hashed out to get to the point of making it a feature request.