4 ms·

I missed that part on my first read. Rereading it makes me think that ethnicity would be used along with age, gender, height, and weight to find a baseline for

I missed that part on my first read. Rereading it makes me think that ethnicity would be used along with age, gender, height, and weight to find a baseline for the 'indicative' factors like heart rate. I agree that once any genetic factors are used as indicative factors we have crossed a dangerous line. I sincerely hope that this phrase in the article was simply worded poorly.

deleted 15y ago
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StavrosK 15y ago
It's much more likely that these inputs will be used as features to train some ML algorithm, rather than that they will actually be used to say "if black then detain".
- pavel_lishin 15y ago
  Yeah, why bother training the computer to do it when TSA employees can cover that angle.
- JoshTriplett 15y ago
  The limitations of machine learning aside, this could actually work quite well if trained properly, because it wouldn't raise the questions that human choices do about bias. Given sufficient information, a machine-learning algorithm can make decisions based on factors that humans would refuse to consider or would intentionally err on the side of not considering.
  - StavrosK 15y ago
    Yes, of course. It could also disregard features completely if it has found that they make no statistical difference.
    - JoshTriplett 15y ago
      Exactly. Most importantly, it can focus exclusively on statistics, rather than guesses.
      - woodson 15y ago
        One problem I see with this is that these statistics involve a lot of human bias. If you train your classifier on crime statistics, it's not based on a 'ground truth' (which is never attainable in a criminal case), but an expert's, i.e. judge's/jury's, decision.
        JoshTriplett 15y ago
        That would indeed produce incorrect training for a classifier of "who should we pull out of line for a search". For that, you'd want to train exclusively on the observable characteristics of the people pulled out of line for a search versus whether you found something.

deleted 15y ago

[deleted]

StavrosK 15y ago

It's much more likely that these inputs will be used as features to train some ML algorithm, rather than that they will actually be used to say "if black then detain".

pavel_lishin 15y ago
Yeah, why bother training the computer to do it when TSA employees can cover that angle.
JoshTriplett 15y ago
The limitations of machine learning aside, this could actually work quite well if trained properly, because it wouldn't raise the questions that human choices do about bias. Given sufficient information, a machine-learning algorithm can make decisions based on factors that humans would refuse to consider or would intentionally err on the side of not considering.
- StavrosK 15y ago
  Yes, of course. It could also disregard features completely if it has found that they make no statistical difference.
  - JoshTriplett 15y ago
    Exactly. Most importantly, it can focus exclusively on statistics, rather than guesses.
    - woodson 15y ago
      One problem I see with this is that these statistics involve a lot of human bias. If you train your classifier on crime statistics, it's not based on a 'ground truth' (which is never attainable in a criminal case), but an expert's, i.e. judge's/jury's, decision.
      - JoshTriplett 15y ago
        That would indeed produce incorrect training for a classifier of "who should we pull out of line for a search". For that, you'd want to train exclusively on the observable characteristics of the people pulled out of line for a search versus whether you found something.