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Direct quote from the article: But where "Minority Report" author Philip K. Dick enlisted psychics to predict crimes, DHS is betting on algorithms: it's buildi

by DanI-S 15y ago

Direct quote from the article: But where "Minority Report" author Philip K. Dick enlisted psychics to predict crimes, DHS is betting on algorithms: it's building a "prototype screening facility" that it hopes will use factors such as ethnicity, gender, breathing, and heart rate to "detect cues indicative of mal-intent."

dkokelley 15y ago
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
  [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.
gjm11 15y ago
Yes, but the article appears to be making this claim without any actual evidence. (See http://news.ycombinator.com/item?id=3084699 http://news.ycombinator.com/item?id=3084699 for slightly more details.)
- VladRussian 15y ago
  you don't need any evidence as it is obvious that ethnicity is part of the feature vector being fed into the system. Even if we imagine unimaginable - that it (together with other highly correlating data like place of birth) is not fed into the system from the start, the face recognition and other input feature vectors generating modules would produce the configuration in the parameter space where basic generic clustering at the pre-processing stage would also naturally cluster along ethnicity lines thus still making ethnicity an input for higher levels of the system. How this input will play inside that giant neural/statistical net wouldn't be possible to say as debugging such nets is an endeavor in itself. Previous implementations of such a pre-crime detection neural nets - using homo sapiens brain as hardware - produced "being black is a probable cause", and nobody was able to debug it in sufficient detail. Would we be able to produce better results using silicon based hardware?

by DanI-S 15y ago

dkokelley 15y ago

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
[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.

gjm11 15y ago

Yes, but the article appears to be making this claim without any actual evidence. (See http://news.ycombinator.com/item?id=3084699 http://news.ycombinator.com/item?id=3084699 for slightly more details.)

VladRussian 15y ago
you don't need any evidence as it is obvious that ethnicity is part of the feature vector being fed into the system. Even if we imagine unimaginable - that it (together with other highly correlating data like place of birth) is not fed into the system from the start, the face recognition and other input feature vectors generating modules would produce the configuration in the parameter space where basic generic clustering at the pre-processing stage would also naturally cluster along ethnicity lines thus still making ethnicity an input for higher levels of the system. How this input will play inside that giant neural/statistical net wouldn't be possible to say as debugging such nets is an endeavor in itself. Previous implementations of such a pre-crime detection neural nets - using homo sapiens brain as hardware - produced "being black is a probable cause", and nobody was able to debug it in sufficient detail. Would we be able to produce better results using silicon based hardware?