[mike_hearn]

> I don't think it's radical that when prompted with something like "Generate photos of doctors", that it's reasonable to return a set of images that shows diversity

Historically Google had a very simple solution to globally differing expectations about query results: IP or account geolocation. Query personalization by geography is one of the biggest quality wins in web search. Generalizing, an AI built with the same values and ethos as classical Google web search would respond to "Generate a photo of doctors" differently depending on where in the world you asked it from.

That solution also fixes many other cases that aren't third rails, like "Show me a good nearby restaurant serving local food" which you can't solve by attempting to hallucinate a non-existent restaurant that serves a menu of every conceivable dish weighted by population size.

It's unclear why this solution wouldn't resolve all their stated concerns, so we might infer that their actual goals differ from their stated goals. For example, influencing the people who use their services.

[changingwords]

That doesn't work well in America, maybe works well in less diverse places like Europe/Asia.

If you're in NYC/SF and you search for "generate photos of doctors", you expect to see people of all colors represented. Yet the training data for a lot of this is based off white-centric Anglo-centric media.

"Good restaurant near me"? There's literally a dozen amazing cuisines around.

All this said, I'm actually not a fan of this forced 'diversity' in results. Just show me the data and hope that we'll have more diverse data sources.

[dkn775]

Another issue I see based on your comment is that segmenting based on locale (diverse mix in SF, white majority in Kansas) is that it can just take what knowledge and norms exist now and harden them.

[mike_hearn]

There's no reason the results have to match the training data. Obviously, the current outputs it generates don't.