I think this is a really good start for the most common types of bias. A few counter-examples that might slip through the cracks of this test: Only examining t

I think this is a really good start for the most common types of bias. A few counter-examples that might slip through the cracks of this test: Only examining the sample without looking at the population of applicants has its limits. Especially as multiple interviews becomes the norm, filters that don't affect the distribution of outcomes will be missed. For example, the person screening resumes might weed out anyone with an ethnic-sounding name. A different person, who is not biased, interviews the candidates. The quality of the candidates accepted will be the same, but the number of minority applicants will be smaller than it should be. Measuring outcomes allows for external biases to distort the results. Start with a company that is biased against women, so that the average female founder is better than the average male. However, that same level of sexism exists in the market, such that the company's performance is hampered due to prejudice against the founder. The VC's bias would be hidden by the counter-bias in the market.