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> the system was more likely to correctly identify men than women and it was “statistically significantly more likely to correctly identify black participants t
by blitzar 7mo ago
> the system was more likely to correctly identify men than women and it was “statistically significantly more likely to correctly identify black participants than participants from other ethnic groups”
Technology has moved on a lot no doubt, however, studies were finding the opposite (and with order of magnitude errors) as recently as 2020 with a lazy google literature search
> these algorithms were found to be between 10 and 100 times more likely to misidentify a Black or East Asian face than a white face
https://jolt.law.harvard.edu/digest/why-racial-bias-is-prevalent-in-facial-recognition-technology https://jolt.law.harvard.edu/digest/why-racial-bias-is-preva...
- deleted 7mo ago[deleted]
- krisoft 7mo agoGiven that these are machine learning algorithms their performance will very much depend on the training dataset. So it is probably not (just) that “technology has moved on a lot”, but that the engineers working on it curated new training sets. It is not entirely unreasonable to think that they too read the paper you are talking about and made measures in an attempt to correct for the effect.
- stuaxo 7mo agoMaybe, or there might be qualities around say contrast and the physical cameras themselves that build this in.
- griffzhowl 7mo agoBut it doesn't seem like these physical factors would have reversed the effect between 2020 and now. Unless you can think of some?
- blitzar 7mo agoCameras are much better now than they were before, all the same everytime you get a "have you seen this person" its a grainy pixelated flat image from CCTV that could easily be one of thousands of people.
- blitzar 7mo agothen in theory, the dataset can be changed to make model error rates "fair" for all intersections of race, gender, age etc.