No. of Recommendations: 7
You raise a good question. There should be a discussion at the national level about the rules of selecting data for building AI.
Well that's fine, but. How do they know whether a particular piece - or source - is AI in the first place? Or, for that matter, just wrong?
The Lancet and the New England Journal of Medicine have both retracted an early published paper on the effectiveness of hydroxychloroquine for treating or preventing Covid, saying that the data set used was insufficient or manipulated (respectively). Nevertheless the story was cited more than 200 times in other scientific papers and journals before the retraction - and here's the interesting part - more than 100 of those cites have not been annotated, retracted, or otherwise changed in any way since then.
Presumably a 'smart enough' AI can figure that out and work backwards, or will the sheer volume of cites be enough to continue spreading misinformation - as the recent experiments by Microsoft, Google and others shows is all too easy.
And for others instances, how will it know if it is merely regurgitating misinformation if it doesn't know that it's merely repeating AI information that was wrong prior? It's turtles all the way down!
There are thousands of examples of how AI screws up, some hilarious, some frightening - and yes, it will get better. But absent some industry standard to watermark or otherwise ignore the falsehoods (so prevalent today), I don't see how this makes things better. Easier, perhaps, but that is a different thing.
