> So how would a ML model find applicants with any kind of disability unless it was freely volunteered in a resume/CV? In machine learning this happens all the

> So how would a ML model find applicants with any kind of disability unless it was freely volunteered in a resume/CV? In machine learning this happens all the time! Stopping models from learning this from the most surprising sources is an active area of research. Models are far more creative in finding these patterns than we are. It can learn that people with disabilities tend to also work with accessibility teams. It can learn that you're more likely to have a disability if you went to certain schools (like a school for the blind, even if you and I wouldn't recognize the name). Or if you work at certain companies or colleges who specialize in this. Or if you publish an article and put it on your CV. Or if you link to your github and the software looks there as well for some keywords. Or if among the keywords and skills that you have you list something that is more likely to be related to accessibility. I'm sure these days software also looks at your linkedin, if you are connected with people who are disability advocates you are far more likely to have a disability. > Or is that the advisory? "Don't do this?" Not so easy. Algorithms learn this information internally and then use it in subtle ways. Like they might decide someone isn't a good fit and that decision may in part be correlated with disability. Disability need not exist anywhere in the system, but the system has still learned to discriminate against disabled people.