Intro to Bias in AI
beluis3d.medium.com
Intro to Bias in AI
1–10 of 20 posts
Re: Intro to Bias in AI
#2In jobdescription.ai [0] I have the challenge of making the job descriptions gender-neutral. I have tested ten job descriptions with Jobvite tool, and the results showed zero biased, but then I started researching more about the gender bias tools and found one study and an article about gender bias [1,2].
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
Re: Intro to Bias in AI
#3In jobdescription.ai [0] I have the challenge of making the job descriptions gender-neutral. I have tested ten job descriptions with Jobvite tool, and the results showed zero biased, but then I started researching more about the gender bias tools and found one study and an article about gender bias [1,2]. Study [1] suggests that men and women will decode wording differently. For instance, women felt that job adverts…
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that, of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
Re: Intro to Bias in AI
#4In jobdescription.ai [0] I have the challenge of making the job descriptions gender-neutral. I have tested ten job descriptions with Jobvite tool, and the results showed zero biased, but then I started researching more about the gender bias tools and found one study and an article about gender bias [1,2]. Study [1] suggests that men and women will decode wording differently. For instance, women felt that job adverts…
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that , of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
And what is the difference between wording and position's description?
Re: Intro to Bias in AI
#5In jobdescription.ai [0] I have the challenge of making the job descriptions gender-neutral. I have tested ten job descriptions with Jobvite tool, and the results showed zero biased, but then I started researching more about the gender bias tools and found one study and an article about gender bias [1,2]. Study [1] suggests that men and women will decode wording differently. For instance, women felt that job adverts…
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that , of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
Isn't the real problem in recruiting that a ton of people who have nothing do to with the job requirements apply?
"Farmer, 45 years old, never finished high school" applies to image processing engineer position.
Re: Intro to Bias in AI
#6Earlier quoted context omitted.
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that , of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
Isn't the real problem in recruiting that a ton of people who have nothing do to with the job requirements apply? "Farmer, 45 years old, never finished high school" applies to image processing engineer position.
Is this a common issue for you ?
In my experience, the most common 'don't match the job requirement' issue is mostly about lack of experience (e.g. out of school and applying to senior position).
And in this case I don't really blame them for trying
Re: Intro to Bias in AI
#7Earlier quoted context omitted.
Isn't the real problem in recruiting that a ton of people who have nothing do to with the job requirements apply? "Farmer, 45 years old, never finished high school" applies to image processing engineer position.
Is this a common issue for you ? In my experience, the most common 'don't match the job requirement' issue is mostly about lack of experience (e.g. out of school and applying to senior position). And in this case I don't really blame them for trying
I was exaggerating, but yes, there are also big mismatches. I think this tends to happen more with big job sites.
Re: Intro to Bias in AI
#8Earlier quoted context omitted.
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that , of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
Isn't the real problem in recruiting that a ton of people who have nothing do to with the job requirements apply? "Farmer, 45 years old, never finished high school" applies to image processing engineer position.
This is more of a problem with men than women, in my experience.
I've even toyed with the idea of compensating for this by having two sets of job ads -- one with very high requirements, and one with just the bare minimum. Then accept applicants for interview from each based on the applicant's statistical propensity to exaggerate their abilities.
Re: Intro to Bias in AI
#9Earlier quoted context omitted.
Maybe it helps to not think about people as statistics. You could write something like "Do you doubt you are right for this job? Please apply anyways, we'd like to know about you!". The problem with that , of course, is that you cannot meet every applicant. So after all, maybe it's not the wording but the position's description that's the problem.
And what is the difference between wording and position's description?
Wording as it is described here seems to be meaningless.
HR persons in general cannot understand the position's requirements. That's why they cling so much on technical details. "What tools do you use? Clang 10.0.1? Ok!" - "Position requires at least 5 years of experience with Clang 10.0.1"
Anything about "challenging", "commitment", etc. are just meaningless, interchangeable fill words. Otherwise, no HR person would dare changing them. I tend to think about them in the same way as fonts.
Re: Intro to Bias in AI
#10In jobdescription.ai [0] I have the challenge of making the job descriptions gender-neutral. I have tested ten job descriptions with Jobvite tool, and the results showed zero biased, but then I started researching more about the gender bias tools and found one study and an article about gender bias [1,2]. Study [1] suggests that men and women will decode wording differently. For instance, women felt that job adverts…
Is this work for some sort of legal compliance reason or just to be more fair or effective? If the latter, perhaps defining the groups as genders is handicapping yourself. A lot of the things you mentioned are more like personality differences that happen to be correlated with gender. I guess it's not up to you to change the scope of your work but more accurate people-types than genders sounds like it would be more ethical.
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.