5 ms·

What do you think the false positive rate is likely to be?

mlyle 7y ago
Depends upon what false negative rate you're willing to tolerate. ;) And I don't know how good of a signal there is. This is pure handwaving. But this type of thing seems like the exact kind of spooky correlation that ML is good at spotting compared to humans.
- YeGoblynQueenne 7y ago
  Machine learning techniques are going to be absolutely awful in detecting something like this, the reason being it's exceedingly rare (at least I'm guessing it is; if we're talking about child sexual abuse by one's own parents, it sure sounds extremely unlikely- but even child abuse in general is probably rare [1]). Machine learning systems are awful at identifying rare events. Like the OP seems to suggest, the false positive rate would most likely be very high. "Spooky" machine learning results happen when a correlation is abundant in a dataset [2]. Otherwise, machine learning techniques will probably miss it altogether. ______________ [1] Quick online search: https://www.inquirer.com/philly/blogs/healthy_kids/What-is-the-rate-of-child-abuse-in-schools--rec-groups.html https://www.inquirer.com/philly/blogs/healthy_kids/What-is-t... [2] The archetypal spooky machine learning story is surely the one about Target sending baby item coupos to a girl in high school before her father knowing she was pregnant: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#7e249cc66686 https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...
  - mlyle 7y ago
    Humans are awful at rare events and vigilance tasks, too. That's part of why we're seeing machine vision and machine learning starting to outperform humans in e.g. grading radiology screening scans. The total incidence of child abuse of all types from infancy to adulthood is on the order of 1 in 3. This is not terrifically rare-- it's of higher prevalence than pregnancy and of positive screening events. A much bigger concern is non-causative correlations. It'd be pretty easy to train ML to be racist or look for e.g. indicators of class, which are correlates of abuse. As to false positive rates-- you can pick your false positive rate to be whatever you want it to be, by twiddling the threshold for a positive result. I'm not sure false positives are of that great of a concern, if the output from a system is a notification to school administrators that they may want to keep an eye out for this student.
  - dragonwriter 7y ago
    > if we're talking about child sexual abuse by one's own parents, it sure sounds extremely unlikely- Child sexual abuse isn't extremely rare and familial abuse is a very large minority of child sex abuse.
- dragonwriter 7y ago
  > But this type of thing seems like the exact kind of spooky correlation that ML is good at spotting compared to humans. How? Particularly, where do you get training data at the required scale?
  - mlyle 7y ago
    You take samples of hundreds or thousands of past students' schoolwork, e.g. submissions of essays for standardized tests. You survey those kids in adulthood about whether and how they were subject to abuse and other types of relevant adversity. You attempt to control the data so that you don't just latch onto other correlates of abuse (e.g. social class).
mschuster91 7y ago
Who cares? The positives must be evaluated by a human anyway.
- tonyedgecombe 7y ago
  Who cares? The people whose lives are ruined by being mis-identified by the system. The positives must be evaluated by a human anyway. Those same people whose lack of competence people are bemoaning throughout these comments.
  - mschuster91 7y ago
    > The people whose lives are ruined by being mis-identified by the system. When a child writes "daddy touches me between the legs" in an essay, it doesn't matter if a human spots it or an AI that forwards it to a human, this needs to be investigated either way. > Those same people whose lack of competence people are bemoaning throughout these comments. It's not a lack of competence that's bemoaned, it's a massive amount of understaffing (and resulting overwork) in teachers and other school resources, as well as a drastic lack of financing because it's easy to cut budgets for schools for politicians as the effects only show up two decades afterwards.
    - tonyedgecombe 7y ago
      Sprinkling some AI over it won't fix those issues, I'd argue it will make them worse as people blindly accept the results. There were some cases in the UK about a decade ago where bugs in software the Royal Mail was using led to incorrect accusations of fraud. People actually went to jail over this, it took years to resolve.
    - mlyle 7y ago
      > When a child writes "daddy touches me between the legs" in an essay, it doesn't matter if a human spots it or an AI that forwards it to a human, this needs to be investigated either way. When a child writes a set of things that individually are not very concerning, they may have cues that could say "hey, this kid, you should maybe keep an eye out for evidence of abuse." Particularly attuned, experienced individuals might spot these cumulative cues, but we all know that this is not all people dealing with children. It's an interesting problem.

Depends upon what false negative rate you're willing to tolerate. ;) And I don't know how good of a signal there is. This is pure handwaving. But this type of thing seems like the exact kind of spooky correlation that ML is good at spotting compared to humans.

YeGoblynQueenne 7y ago
Machine learning techniques are going to be absolutely awful in detecting something like this, the reason being it's exceedingly rare (at least I'm guessing it is; if we're talking about child sexual abuse by one's own parents, it sure sounds extremely unlikely- but even child abuse in general is probably rare [1]). Machine learning systems are awful at identifying rare events. Like the OP seems to suggest, the false positive rate would most likely be very high. "Spooky" machine learning results happen when a correlation is abundant in a dataset [2]. Otherwise, machine learning techniques will probably miss it altogether. ______________ [1] Quick online search: https://www.inquirer.com/philly/blogs/healthy_kids/What-is-the-rate-of-child-abuse-in-schools--rec-groups.html https://www.inquirer.com/philly/blogs/healthy_kids/What-is-t... [2] The archetypal spooky machine learning story is surely the one about Target sending baby item coupos to a girl in high school before her father knowing she was pregnant: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#7e249cc66686 https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...
- mlyle 7y ago
  Humans are awful at rare events and vigilance tasks, too. That's part of why we're seeing machine vision and machine learning starting to outperform humans in e.g. grading radiology screening scans. The total incidence of child abuse of all types from infancy to adulthood is on the order of 1 in 3. This is not terrifically rare-- it's of higher prevalence than pregnancy and of positive screening events. A much bigger concern is non-causative correlations. It'd be pretty easy to train ML to be racist or look for e.g. indicators of class, which are correlates of abuse. As to false positive rates-- you can pick your false positive rate to be whatever you want it to be, by twiddling the threshold for a positive result. I'm not sure false positives are of that great of a concern, if the output from a system is a notification to school administrators that they may want to keep an eye out for this student.
- dragonwriter 7y ago
  > if we're talking about child sexual abuse by one's own parents, it sure sounds extremely unlikely- Child sexual abuse isn't extremely rare and familial abuse is a very large minority of child sex abuse.
dragonwriter 7y ago
> But this type of thing seems like the exact kind of spooky correlation that ML is good at spotting compared to humans. How? Particularly, where do you get training data at the required scale?
- mlyle 7y ago
  You take samples of hundreds or thousands of past students' schoolwork, e.g. submissions of essays for standardized tests. You survey those kids in adulthood about whether and how they were subject to abuse and other types of relevant adversity. You attempt to control the data so that you don't just latch onto other correlates of abuse (e.g. social class).

mschuster91 7y ago

Who cares? The positives must be evaluated by a human anyway.

tonyedgecombe 7y ago
Who cares? The people whose lives are ruined by being mis-identified by the system. The positives must be evaluated by a human anyway. Those same people whose lack of competence people are bemoaning throughout these comments.
- mschuster91 7y ago
  > The people whose lives are ruined by being mis-identified by the system. When a child writes "daddy touches me between the legs" in an essay, it doesn't matter if a human spots it or an AI that forwards it to a human, this needs to be investigated either way. > Those same people whose lack of competence people are bemoaning throughout these comments. It's not a lack of competence that's bemoaned, it's a massive amount of understaffing (and resulting overwork) in teachers and other school resources, as well as a drastic lack of financing because it's easy to cut budgets for schools for politicians as the effects only show up two decades afterwards.
  - tonyedgecombe 7y ago
    Sprinkling some AI over it won't fix those issues, I'd argue it will make them worse as people blindly accept the results. There were some cases in the UK about a decade ago where bugs in software the Royal Mail was using led to incorrect accusations of fraud. People actually went to jail over this, it took years to resolve.
  - mlyle 7y ago
    > When a child writes "daddy touches me between the legs" in an essay, it doesn't matter if a human spots it or an AI that forwards it to a human, this needs to be investigated either way. When a child writes a set of things that individually are not very concerning, they may have cues that could say "hey, this kid, you should maybe keep an eye out for evidence of abuse." Particularly attuned, experienced individuals might spot these cumulative cues, but we all know that this is not all people dealing with children. It's an interesting problem.