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Defining equity in hiring (and everywhere else) is not as easy as it may sound - "equity" is not an established term and may be carry different meanings for different people.
I can see at least three ways to define equitable hiring process:
1) All groups are proportionally represented.
2) Candidates from all groups have the same false negative rate (fit candidates are not hired equally across all groups).
3) Candidates from all groups have the same false positive rate (unfit candidates are hired equally across all groups).
The funny fact is that implementing #2 and #3 looks suspiciously similar to affirmative action. Why?
Let say we have an imperfect proxy metric that defines future success and this metric has different distribution for different groups.
A really simplified example would be standard tests' scores - let's say we have group A with a limited resources to practice tests, tutors etc. The more talented students will still score higher.
On the other hand, in group B, where students have access to everything they can dream of, even dumb students can score higher than the best students from the group A.
However, in each respective group the score can still be a predictor of a long-term success - the best students from each group will achieve similar results in their careers. (Yes, yes, I am going on a limb here, but this is a simplified example - so please bear with me.)
If we define equity as #2 and #3 - then we would need to define different thresholds for different groups. It may sound counterituitive - but different thresholds can lead to equalizing opportunities for equally talented people with different access to resources, when we use an imperfect proxy metric to measure a probability of a future performance.
Any flaws in this logic? Comments/feedback are welcome.
I can see at least three ways to define equitable hiring process:
1) All groups are proportionally represented.
2) Candidates from all groups have the same false negative rate (fit candidates are not hired equally across all groups).
3) Candidates from all groups have the same false positive rate (unfit candidates are hired equally across all groups).
The funny fact is that implementing #2 and #3 looks suspiciously similar to affirmative action. Why?
Let say we have an imperfect proxy metric that defines future success and this metric has different distribution for different groups.
A really simplified example would be standard tests' scores - let's say we have group A with a limited resources to practice tests, tutors etc. The more talented students will still score higher.
On the other hand, in group B, where students have access to everything they can dream of, even dumb students can score higher than the best students from the group A.
However, in each respective group the score can still be a predictor of a long-term success - the best students from each group will achieve similar results in their careers. (Yes, yes, I am going on a limb here, but this is a simplified example - so please bear with me.)
If we define equity as #2 and #3 - then we would need to define different thresholds for different groups. It may sound counterituitive - but different thresholds can lead to equalizing opportunities for equally talented people with different access to resources, when we use an imperfect proxy metric to measure a probability of a future performance.
Any flaws in this logic? Comments/feedback are welcome.