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I agree with you and we're not trying to infer new things about people that aren't directly in the resume. Our approach is to take a more (unfortunately?) prac

by cl42 6y ago

I agree with you and we're not trying to infer new things about people that aren't directly in the resume. Our approach is to take a more (unfortunately?) practical approach to this -- most recruiters and hiring managers simply scan most resumes for keywords, and most applicant tracking systems do the same... So it's not about predicting something from the resume, so much as making the matching process more flexible, given that most people doing the initial screening aren't doing a good job. In other words: make a very imperfect process slightly less bad today, so good candidates don't get missed. And of course, you still need to do the full set of interviews afterwards! We're not saying this is any sort of silver bullet.

andy99 6y ago
This is a point worth emphasizing. So many proposed "AI" use cases insist on trying to make some kind of end-to-end prediction, rather than let ML do something it is well positioned to do (e.g.) search more effectively, and let a person make the judgement for the things that aren't so readily predicted. My analogy is a weather forecast. Regular forecasts give you the temperature and precipitation, and let you decide what to do based on this. If meteorologists instead black boxed the weather and just tried to tell people what kind of clothes to wear every day, I suspect people would start ignoring the forecast. But this is how so many ML use cases are set up. I'm happy to see an application that respects ML for what it can do and not try and shoehorn it into the "prediction machines" paradigm.
- cl42 6y ago
  I love that analogy so much! Thank you for that. We'll definitely clarify that in future posts as well. We did our best to provide an example in the post, but it's tough. We got into talent/recruiting because I'm a former data science manager (my last startup was an AI one as well) and it always bothers me how little intentionality and awareness there is around limitations of AI in AI-driven products. Doesn't help that marketing teams tend to just promote the idea that "AI will solve everything", either!

by cl42 6y ago

andy99 6y ago

This is a point worth emphasizing. So many proposed "AI" use cases insist on trying to make some kind of end-to-end prediction, rather than let ML do something it is well positioned to do (e.g.) search more effectively, and let a person make the judgement for the things that aren't so readily predicted. My analogy is a weather forecast. Regular forecasts give you the temperature and precipitation, and let you decide what to do based on this. If meteorologists instead black boxed the weather and just tried to tell people what kind of clothes to wear every day, I suspect people would start ignoring the forecast. But this is how so many ML use cases are set up. I'm happy to see an application that respects ML for what it can do and not try and shoehorn it into the "prediction machines" paradigm.

cl42 6y ago
I love that analogy so much! Thank you for that. We'll definitely clarify that in future posts as well. We did our best to provide an example in the post, but it's tough. We got into talent/recruiting because I'm a former data science manager (my last startup was an AI one as well) and it always bothers me how little intentionality and awareness there is around limitations of AI in AI-driven products. Doesn't help that marketing teams tend to just promote the idea that "AI will solve everything", either!