4 ms·

Ah yes, the classic "we fed a bunch of real world data to an AI, said AI started finding patterns in the data and making decisions based on those patterns, so w

by bakugo 4y ago

Ah yes, the classic "we fed a bunch of real world data to an AI, said AI started finding patterns in the data and making decisions based on those patterns, so we shut it down because the patterns weren't politically correct" story. How many times have we heard this one?

ok_dad 4y ago
Are you arguing people named Jared with high school lacrosse experience are the pinnacle of human achievement?
- bakugo 4y ago
  I don't know about the Jared thing but I wouldn't be surprised at all if a high school sport ended up being a legitimate indicator of future job performance, even if not a very significant one.
  - ok_dad 4y ago
    I can agree that high school sports players are probably hard workers, sure. Clearly, hard work is a big part of doing well in life, since life has a lot of hard work (some of it is fun, too). However, you can't just blindly "follow the data" unless you have literally accounted for all of the data that is currently not collected, which we don't do because we cannot collect every metric relevant to this issue.
    - xboxnolifes 4y ago
      > However, you can't just blindly "follow the data" unless you have literally accounted for all of the data that is currently not collected, which we don't do because we cannot collect every metric relevant to this issue. Why not? Isn't that basically what the status quo is for hiring? Why does AI hiring have such a larger bar?
- fluoridation 4y ago
  The statement verbatim was >the algorithm found two factors to be most indicative of job performance: their name was Jared, and whether they played high school lacrosse That doesn't mean that the "pinnacle of human achievement" necessarily is named Jared and played lacrosse in high school. It means that if you hire people who meet those conditions after filtering using X additional criteria, on average you'll get a more performant workforce than if you hire from the general population after filtering using X additional criteria. The algorithm found an objective truth. Given the data provided and the question asked, the answer should be indisputable. The only reason to reject it should be because someone thinks the answer is incorrect, but that means that either the algorithm is incorrect, the data is incorrect, or the question was poorly phrased.
  - ok_dad 4y ago
    > The algorithm found an objective truth The "objective truth" for this specific data is that out of all people hired dudes named jared and/or lacrosse players were more high achieving. That is where you get it wrong: the data is reflective of the bias in the current process, to remove that bias you can't simply reinforce the current behaviors via AI math-washing the same data.
    - fluoridation 4y ago
      That's silly. That would imply that the hiring process is actually very good at predicting performance, and it's biased towards hiring highly performant people named Jared and less performant people not named Jared, and it's not hiring highly performant people not named Jared and less performant people named Jared.
      - ok_dad 4y ago
        What are you talking about? I was saying that the biases we have today in our hiring cannot be eliminated by analyzing that data and hiring the same types of people that are "high performance", because you'll be reinforcing the biases we have, rather than eliminating them.
      - 4y ago
- deleted 4y ago
  [deleted]

by bakugo 4y ago

ok_dad 4y ago

Are you arguing people named Jared with high school lacrosse experience are the pinnacle of human achievement?

bakugo 4y ago
I don't know about the Jared thing but I wouldn't be surprised at all if a high school sport ended up being a legitimate indicator of future job performance, even if not a very significant one.
- ok_dad 4y ago
  I can agree that high school sports players are probably hard workers, sure. Clearly, hard work is a big part of doing well in life, since life has a lot of hard work (some of it is fun, too). However, you can't just blindly "follow the data" unless you have literally accounted for all of the data that is currently not collected, which we don't do because we cannot collect every metric relevant to this issue.
  - xboxnolifes 4y ago
    > However, you can't just blindly "follow the data" unless you have literally accounted for all of the data that is currently not collected, which we don't do because we cannot collect every metric relevant to this issue. Why not? Isn't that basically what the status quo is for hiring? Why does AI hiring have such a larger bar?
fluoridation 4y ago
The statement verbatim was >the algorithm found two factors to be most indicative of job performance: their name was Jared, and whether they played high school lacrosse That doesn't mean that the "pinnacle of human achievement" necessarily is named Jared and played lacrosse in high school. It means that if you hire people who meet those conditions after filtering using X additional criteria, on average you'll get a more performant workforce than if you hire from the general population after filtering using X additional criteria. The algorithm found an objective truth. Given the data provided and the question asked, the answer should be indisputable. The only reason to reject it should be because someone thinks the answer is incorrect, but that means that either the algorithm is incorrect, the data is incorrect, or the question was poorly phrased.
- ok_dad 4y ago
  > The algorithm found an objective truth The "objective truth" for this specific data is that out of all people hired dudes named jared and/or lacrosse players were more high achieving. That is where you get it wrong: the data is reflective of the bias in the current process, to remove that bias you can't simply reinforce the current behaviors via AI math-washing the same data.
  - fluoridation 4y ago
    That's silly. That would imply that the hiring process is actually very good at predicting performance, and it's biased towards hiring highly performant people named Jared and less performant people not named Jared, and it's not hiring highly performant people not named Jared and less performant people named Jared.
    - ok_dad 4y ago
      What are you talking about? I was saying that the biases we have today in our hiring cannot be eliminated by analyzing that data and hiring the same types of people that are "high performance", because you'll be reinforcing the biases we have, rather than eliminating them.
    - 4y ago
deleted 4y ago
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