Study [1] suggests that men and women will decode wording differently. For instance, women felt that job adverts with masculine-coded language were less appealing and that they belonged less in those occupations. Some masculine-coded words are challenging and lead while some feminine-coded words are support and commitment.
That does not mean to imply that men lack the ability to be supportive or collaborative, nor women lack leadership or challenging skills “But, based on data analytics on the kinds of jobs men and women apply for, research shows that the adjectives matter.”
Article [2] supports the study and added "Many women won’t apply for a job unless they meet almost all of the listed requirements" so the list of requirements matter as well.
I plan to research more to better understand the gender bias in terms of wordings before implementing tools to create a feedback loop to improve the algorithm.
[0] https://www.jobdescription.ai
[1] http://gender-decoder.katmatfield.com/static/documents/Gauch...
[2] https://www.forbes.com/sites/hbsworkingknowledge/2016/12/14/...
edit: to provide more information instead of links with no context
Telling the truth instead of manipulating people? Say that it is challenging if it is. Say that it is supportive if it is? List the requirements that are required?
When I mentioned gender-neutral job descriptions, I referred to ads to balance using this gender-coded language instead of eliminating any of them. Bias towards any gender is gender bias.
And I agree with your point regarding requirements, that's why the article mentioned writing the actual requirements instead of a long list of things that are not in that job.
"Farmer, 45 years old, never finished high school" applies to image processing engineer position.
This comment is motivated by personal experience. I'm a man but I used to very much "won’t apply for a job unless [I] meet almost all of the listed requirements". It felt great early on because I almost always got hired for any job I applied for! I'd guess that the vast majority of jobs I've applied for in my entire life have at least reached interview stage. But I also haven't got far in my career and fear it may be over for good now because I was too cautious and underconfident. Only recently, I've learned to disregard all the "preferred" criteria and apply to interesting jobs if I have all of the "required" or "must" criteria. But now I wonder if even that's being too strict.
As for your point regarding "meeting all requirements", I can relate to it. This supports the theory of listing the absolute requirements to get more applications instead of copy/pasting everything from other job descriptions. This improvement in itself makes it a win-win situation.
I came across a good Twitter thread[1] explaining some of these other types of bias -- a lot of them come down to various ways in which model decisions end up impacting performance on the "long tail" of data (i.e., the less frequent categories and groups) long before they impact the bulk of the distribution. This means overall performance may be minimally impacted (or even improved), but performance for subgroups can be drastically reduced.
Anyway, the thread is definitely worth a read, and it links to many sources for further reading.
[1] https://twitter.com/sarahookr/status/1361373527861915648
