One of the challenges that the experts constantly point to is that the AI/ML here on iNat sometimes gives a false sense of confidence of an identification.
Problem statement: many times I and other newbies think we can identify an organism to species level, because the AI/ML makes a suggestion, a visual review of the suggestion looks accurate, as well as the map of sightings and time of year indicators. I later find out that the organism can only truly be identified under microscope or by dissection. This fact seems to be common knowledge among the experts. If it is common knowledge, we should be able to signal this difficulty to users.
Possible solution: Add a “visually identifiable” parameter at the appropriate taxonomic level, which would be seeded by experts. This could indicate whether an organism can truly be identified by photo, or requires some degree of magnification of specific parts, up to requiring a microscope, or dissection.
A launch-and-iterate approach could start with only this parameter, which would help set user expectation (perhaps combined with a simple UI flagging about difficulty of the ID). Over time, the AI/ML algorithm could be adjusted to consider this new parameter in conjunction with location, time of year, etc. i.e. you could infer that it is very likely to be species X, even if species X and X’ look identical, but X’ only occurs 2000 km away in a different habitat. As a final iteration, this could be turned in to a confidence level for the ID.