Personal and Professional Context and Disclaimers
I work at the Taskar Center for Accessible Technology (TCAT) at the University of Washington. To be clear, AI (and specifically/especially its use in the OSM ecosystem) is without a doubt a core part of our (though not my) focus.
This post is intentionally made under my personal account because it is a sharing of my own personal views. These opinions are, naturally, partly the product of my experience working directly with the AI-produced outputs of parts of our internal tools. My perspective is also informed by the uncompensated hobby work that I’ve done in OSM over the last year and my own views on AI and related technologies.
Thank you for engaging here (and elsewhere) with the community. That’s definitely a step in the right direction. 
One thing that frustrates me when these topics come up is that the low quality of the final output often complicates discussions about the quality of specific parts of the process - I think it’s clear that the machine-generated ways produced by the currently available tools in this space are not at the quality level desired by many for inclusion directly into OSM.
If you develop a tool which can accurately and efficiently recognize specific features from aerial imagery and pass those detections to humans for verification - great! If you develop a tool which can take those verified detections and use them to insert low-quality geometry directly into OSM - that’s not so great.
Please consider narrowing the scope of the contributions that your tool makes to OSM and repositioning OSM within your project. In the swimming pools example, the human-vetted detections could result in swimming pool nodes (centroids calculated from the AI-inferred geometry) being contributed back to OSM. If you want to use the inferred geometry for something, such as answering “What percentage of the area of Example Town is covered by swimming pools?”, then that’s fine - the geometry itself just doesn’t belong in OSM. One could always make something like a follow-up MapRoulette challenge for “Upgrade leisure=swimming_pool nodes to areas” or similar! @mvexel might be able to help you there.
Additional reflections, based on work experience
[Again note that this work was mostly done before I started working for UW, and it’s not my focus - so “we” here generally means “our team, not including me”]
We (TCAT) made heavy use of AI to create the OS-CONNECT dataset (viewer) for the WA Proviso project, and we’re not shy about that - refer to the first sentence on the project’s main page:
Under the directive of the state legislature and using innovative technology by the Taskar Center for Accessible Technology, Washington is now creating an AI-generated, human-vetted network graph inventory of sidewalks across Washington State—the OS-CONNECT dataset.
This data is not added directly to OSM, because that’s not where it’s useful. It doesn’t mean it’s not useful (it is!) and that doesn’t mean we don’t contribute directly to OSM (we do!) but it does mean that we (meaning both our team and the broader OSM community) recognize AI-generated data, even when inextricably related to OSM, shouldn’t be OSM.
Also, you may be interested in [2303.02323] APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks.
I respect and appreciate that you are excited about the technology you’re working with and want to use it to improve OSM - that’s a wonderful thing, and I hope that you’re not discouraged from continuing to work in this space because I definitely see the potential of these tools which your project highlights.