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Thomas Ball

Kaiser-
Wish I had known of this event! But for all the bandwagon hype, smoke and mirrors was there any discussion -- beyond bias -- of the methodological limits of AI, DL, NNs, etc.?
Thanks...

Antonio Rinaldi

I remember a tweet about the double (un)known classification. Data is the known known. Sampling error is an example of known unknown. The bias speakers deal on is the known unknown. But there is also another bias, the unknown unknown, that cannot be repaired. The first one cannot be repaired only when assuming politically correctness as a dogma.

Kaiser

AR: Thanks for the add. I recall that comment. I think that quote is only approximately useful. "Data is the known known" doesn't cut it for me as it does not deal with fake data, bad data, missing data, etc. Sampling error is not a known unknown; it is a measure of variability caused by random sampling, which does not apply to "data exhaust" type of data.

I recommend people read Chapter 3 of my book Numbers Rule Your World - the section about dealing with racial bias in SAT testing - to understand what the issue of "bias" is about. Also, the Prologue of Numbersense also deals with this issue.

TB: That meeting is not the place to go to hear about limitations. Under the "Real Challenges" section, I collected the several episodes in which someone pointed out limitations. It does appear that the one problem acknowledged by all is "bias". The general mood in the room is that there is an engineering solution to all problems.

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