Posted by sizzle 7 hours ago
They find "disparate impact" of pymetrics across racial groups, but it doesn't seem like they controlled for anything.
>If the AI had recommended Black and Asian candidates at the same rate as it recommended the most-favored group (typically white applicants), 40,000 more of their applications would have advanced to the next stage of hiring.
I don't think this is the right benchmark here, or at least, it would be very interesting if the actual outcome, offer or rejected, was considered at the end.
For the AI study real data from "3.4 million people who submit 4 million job applications to 1,700 job postings across 150 employers and 11 industry sectors" was used.
https://www.yahoo.com/news/us/articles/california-judge-upho...
AI works by learning patterns. So it will become bias by just learning from factors like education history, schools attended, employment history, ZIP codes, or geographic location. Those 3 factors alone are an easy proxy for race.
And if you add names into the equation (if the AI was trained without removing applicant names), the model can become even more bias.
I guess this one just compounds.
I see nothing that shows any system was making a decision on race. How is the race being presented to the AI?
All this is showing from what I can see, is that certain groups of people were more often denied a next step in the process - but why?
Was the AI going by spelling and grammar? Were there names that were different but the rest of the resume was exactly the same? Were there pictures?
There were mentions that the rate of each group may be more prominent in the data when you split apart different types of jobs instead of all jobs in aggregate.. One could read that like it's inferred; that more warehouse jobs are offered to a race and less admin jobs.. but that same would happen if AI was more focused on perfect grammar for one job and it was not as much of a factor for a warehouse job.
Also if the people applying for the various jobs were self selecting, acceptance percentages this would skew things based upon which ones were applied / not applied to right?
There are so many ways you could draw conclusions like this from data, however correlation is not causation, yet this seems to say it is.
I feel this is an important thing to watch, but Stanford may not be the place to trust with 'Policy Recommendations' as it's very unclear there is any proof that 'AI Hiring Tools Yield Racial Bias and Systemic Rejection' from this study and paper.
PS - now that I see the HN title did not have the word "can" in it, and the title of the article is actually "Tools Can Yield" - maybe that is less accusing and more noting.
Only 40% self report gender/race
no resume data, no education information, degrees, schools, GPA, major, work experience, skills/certifications
Zero job qualifications