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That it is a “game of whack-a-mole” isn’t clear to me, and you are asserting so without any backing, which is why I asked if anyone has real-world examples. Ci
by mieseratte 7y ago
That it is a “game of whack-a-mole” isn’t clear to me, and you are asserting so without any backing, which is why I asked if anyone has real-world examples.
Citing an example where they don’t provide gender but then provide “went to an all-women college” is not an example of proving non-protected information which results in protected-class discrimination, it is an example of telling an ML system protected information in a roundabout way.
- michaelt 7y agoAge is a protected class, and it's extremely difficult to sanitise age from a resume. Let's say the candidate got a BS in EECS in 1987, they've had 6 jobs since graduating, and their first job was cost-optimising floppy disk drives. Do you feed the ML system this data, which is clearly correlated with age? Do you keep the number of jobs but delete the duration, responding to 6-jobs-in-3-years the same as 6-jobs-in-30-years? How do you sanitise the age-correlated data out of job descriptions naming old tech or defunct employers? Do you delete all but the last 5 years of their employment history, assigning no value to experience beyond that?