| As Prof. Bohnet herself points out, the HR dept. should do more A/B testing of its practices instead of relying on the gut feel.
Hence the gradual increase in the prevalence of "people analytics" (apart from allure of sucking in all the data-points you can). But there are a few problems:
1. A/B testing in talent mgmt practices is costly in terms of time-investment.
- Final, long term impact of such experiments are not visible to the org for, well, a long while.
Sure, may be some short term impacts like increased diversity - may be visible in the short term.
But is that worth all the experimentation for all the firms except the largest ones like FANG? The experiment cost is probably not worth the value for the smaller orgs.
2. Vetting people for skillsets and qualifications only, masking the personal details - like in the OP on Helsinki, is a good start.
But it does not address the biases on the historical disadvantages like not coming from a pedigreed organization because of not having been to a pedigreed school.
Think of being ranked lower in the applications to Netflix because you are not coming from Google because you didn't go to Stanford.
Blind -audition technique would be the best in situations like these as the focus would be on demonstrating the skill needed and not the "qualifications" (read - pedigree).Such skill tests can probably be done easily for programming and other situations but a lot of roles do not have a strictly defined evaluation criteria as the KPIs are...well... fuzzy.
Or at least not something which can be demonstrated in a short "test".
Think sales / pro-management. You can not demonstrate in a test if you are a good enough sales person who can meet the revenue projections ("sell this pencil to me" doesn't count). Issue here is lack of a easily reproducible and reliable signal for your future success for such roles, thereby increasing the dependency on past history - and by association, your pedigree. Of course there will be other issues like isolating performance factors into "chance" and "skill" in a noisy dataset that is usually available, if at all.
Or hiring mindset which focuses on the required skillset (what is needed to get the job done) but the "best person" for the job which muddies the waters by adding evaluation factors which are really not material to performance on the job at hand. But if the cost of experimentation can be reduced by devising quick tests for generating reliable signals independent from historical background, it would be awesome! |