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Hi Chris --- In the earlier thread, it seemed like some people were reaching different conclusions because they were using different definitions of "bias". I t
by nkurz 11y ago
Hi Chris ---
In the earlier thread, it seemed like some people were reaching different conclusions because they were using different definitions of "bias". I think my working definition would be something like "there existed in the actual applicant pool a subset of unfunded female founders who should have been statistically expected (given the information information available to the VC's at the time of decision) to outperform an equal sized subset of male founders who did in fact receive funding".
Alternatively (and I don't think equivalently?) one could reasonably take bias to mean "Given their prejudices, if the same VC's had been blinded to the sex of the applicants, they would have made funding choices resulting in higher total returns than the sex-aware choices they actually made." I'm sure there are many other ways of defining "bias". Could you define what would need to be true for your test to show that "the VC process is biased against female founders"?
- SamReidHughes 11y ago> (and I don't think equivalently?) I think that's equivalent (assuming there are no efficacy losses of blinding, of course). If you've got the two subsets in question, then they could make a choice with higher expected returns. If they can make a choice with higher expected returns, then there exist the subsets in question. (Let me skip the handwaving about continuity and measurability, please.) Those definitions are good because they even describe bias in a scenario where VC's are less accurate at identifying the ability of one group versus another, even if both had equal distributions of "true observable ability" and equal-sized applicant pools, and if the VC's decisions still resulted in equal proportions of each group getting funded.