| I wonder if this will be a net-loss. I'm not discussing whether or not Amazon's system has merit, nor if algorithmic performance evaluation is inherently bad. Look at standardized testing in the USA (SAT, ACT) - there is tons of coaching and preparation specific to the test that isn't training anyone on the material. Instead, we know how much time to spend per question, how to quickly eliminate possible answers on a multiple choice to improve odds when guessing, what patterns in the language a question exist, etc. I think this is so so similar to customer support and that issue big corporations (and especially us, as their customers, face) which is that quality evaluation is not something that can be boiled down to quantitative measurement. Sure, it can weed out many common problems, but it's just not worth it. Judging workers solely based on some rubric will create a class of people who min-max the system and will lower the overall quality of the service provided, while disenfranchising the best employees. On the other hand, maybe this will help move these opaque and (almost certainly) biased processes towards some better baseline. |
But this misses the point entirely. Because these metrics and criteria are not transparent, the people affected by them have no recourse. There is no specific thing they can point to that is unreasonable about the standards by which they are judged, because there is nothing specific accessible to them at all. With no transparency, the "algorithm" could be some stupid nonsense, or something highly illegal, or something boring and human like managers playing favorites by up/downvoting employees on a secret app. The intention of this regulation is to shine a light on these practices, so that both the people affected and regulators can see how stupid and unethical they probably are - instead of speculating - and do specific things about it