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No, she's saying that if the input data is itself skewed, then one method of fixing the model you generate is to apply corrections to the calibrating parameters
by ABCLAW 9y ago
No, she's saying that if the input data is itself skewed, then one method of fixing the model you generate is to apply corrections to the calibrating parameters.
Imagine we have a 50/50 population of purple and orange people. We know that purple offenders are caught twice as often for a certain crime, and 10 purple offenders are caught and 5 orange offenders are caught. If we run our data and our model predicts that we're getting substantially more purple offenders, then we know our model is wrong - it isn't taking into account the skew due to differential enforcement.
How do you fix this? She gives 3 methods. 1) Change the params to normalize, 2) resample and get better data, 3) use causal reasoning and other data to plug in the holes.
- PeterStuer 9y agoBut in reality not all feature dimensions are orthogonal. You can't make the same 'adjustment' for any potential trait bias. Correcting for the purple/orange thing might skew the data on square and round heads, and correcting for that might skew it on pointy and rounded ears and so on and on. The intention is laudable, but many a road to hell is paved with good intentions.
- visarga 9y agoSo they got to balance data on all sub-combinations of features "orange round head with pointy ears" vs "purple square head rounded ears", etc - which are exponential in the number of features. The curse of dimensionality.
- pacala 9y agoHow do you quantify "skew due to differential enforcement"? The proposal is to posit some value for "skew due to differential enforcement", then massage the results to match the baseless bias.
- bobcostas55 9y agoThe data[0] shows that the race distribution of the assailant in crime reports is about the same as the race distribution of arrestees. There's no support for the differential enforcement hypothesis. [0] https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=256035 https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=2560...
- ucaetano 9y ago> The data[0] shows that the race distribution of the assailant in crime reports is about the same as the race distribution of arrestees. There's no support for the differential enforcement hypothesis. Unless the reports are also influenced by race, such as a police officer not stopping a white person who was a criminal because he/she is white, but over-inspecting non-white people.
- tschwimmer 9y agoIncidentally "Driving While Black" is a fairly well-known phenomenon.[1] [1] https://en.wikipedia.org/wiki/Driving_while_black https://en.wikipedia.org/wiki/Driving_while_black
- ucaetano 9y agoExactly. Let's say cops stop 10% of blacks for "looking suspicious" (aka, being black), but only 1% of whites. Now say that 10% of both whites and blacks are criminals. And they always get identified if stopped, and eventually arrested and sentenced. An that the population is evenly split between blacks and whites. (1) Criminal prevalence: 50% B, 50% W (2) Police stops: 91% B, 9% W (3) Criminals caught (reports): 91% B, 9% W (4) Incarceration: 91% B, 9% W Now remember that only (3) and (4) are available to the public, so one might look and say that the problem isn't that blacks are incarcerated differently, but that blacks commit more crimes, when they don't.