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I run a design/development agency in Europe. I'm the only male (of <10 employees). We had an applicant for a development job who said he was 50+ and had been s

by throwaway101916 11y ago

I run a design/development agency in Europe. I'm the only male (of <10 employees). We had an applicant for a development job who said he was 50+ and had been self-employed several times. In the end we decided not to hire him. The age was a factor, but not in the way you'd think. The primary reason was that he proved himself to be a very bad fit for the team and the kind of clients we work with. He attempted to dominate every conversation to prove his seniority (or assert his potential if it was on a subject he had no prior experience with). I'd like to think his age wasn't a factor at all and most of his behaviour can instead be attributed to his prior experience being self-employed, but I'm not sure whether this is true or not. I don't think age is a valid argument any more than race or gender but I'd like to think that these are just unfortunate proxies for the actual reasons companies don't want to hire candidates. I've turned down a lot of "foreigners" because their language skills were insufficient for the requirements of the job. I've turned down "men" because of their macho-like behaviour towards the women on the team and I've turned down "old people" because they treated younger team members less respectfully. None of these traits are representative of all potential candidates in each group, but they tend to be common enough that you tend to become more sensitive to them, especially when hiring people from that group. This leads to unfair bias, but short of anonymizing CVs (which can be extremely difficult) I don't see any fool-proof way of avoiding that. There's a very thin line between due dilligence and unfair bias.

JoeAltmaier 11y ago
Discrimination is so popular because it works so well. As mentioned, there are cultural values that you can quite often predict just from {being a woman/black/old}. A primary mechanism to combat this is, to get folks to evaluate each person as a person. To do this, an effort has to be made to recognize and cancel out any preconceived notions may have been formed from incidental cues like age. I'm glad to see anecdotally that candidates were turned down for real reasons, at least so far. I'd advice being very diligent at continuing to require real criterion in hiring decisions. For instance, anonymizing CVs is not at all hard - why consider it a last resort? E.g. put a piece of tape over the name when reading a pile of them.
- pluma 11y ago
  This is actually a big problem with deep machine learning based algorithms: they're guaranteed to be discriminatory but there is no obvious mechanism for making them obey laws that prohibit discrimination (which often means they get away with it based on a technicality). In a perfect world this wouldn't be a problem because you'd just fix the root issues (i.e. eliminate poverty and crime in ethnic groups, fill leading roles in tech with women, etc etc). In the real world, things are not that straightforward (especially in the US where a lot more data can be collected and used to feed the algorithms than elsewhere).
rachelgli 11y ago
Hi there, Rachel from Medium. This is a useful insight, especially about how potential hires treat others. Feel free to repost this as a response on the original Medium post.

JoeAltmaier 11y ago

Discrimination is so popular because it works so well. As mentioned, there are cultural values that you can quite often predict just from {being a woman/black/old}. A primary mechanism to combat this is, to get folks to evaluate each person as a person. To do this, an effort has to be made to recognize and cancel out any preconceived notions may have been formed from incidental cues like age. I'm glad to see anecdotally that candidates were turned down for real reasons, at least so far. I'd advice being very diligent at continuing to require real criterion in hiring decisions. For instance, anonymizing CVs is not at all hard - why consider it a last resort? E.g. put a piece of tape over the name when reading a pile of them.

pluma 11y ago
This is actually a big problem with deep machine learning based algorithms: they're guaranteed to be discriminatory but there is no obvious mechanism for making them obey laws that prohibit discrimination (which often means they get away with it based on a technicality). In a perfect world this wouldn't be a problem because you'd just fix the root issues (i.e. eliminate poverty and crime in ethnic groups, fill leading roles in tech with women, etc etc). In the real world, things are not that straightforward (especially in the US where a lot more data can be collected and used to feed the algorithms than elsewhere).

rachelgli 11y ago

Hi there, Rachel from Medium. This is a useful insight, especially about how potential hires treat others. Feel free to repost this as a response on the original Medium post.