: Anonymizing as a Service
- Designed for equal opportunity hiring / funding.
- 3rd party fields candidates, scrubs all data that is proven to induce bias
- Doesn't address in-person stage, but any improvement welcome
Feel free to tell me why it's a dumb idea.
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You would use historical resumes of successful candidates as the data inputs. Definitely a chance that all the data in the world wouldn't produce patterns, but seems worth exploring.
The obvious filters are easy: name, gender, ethnicity, geography, etc.
With enough data, I think you could identify and filter the 20% of resume terms / qualities that induce 80% of the bias.Sure but if you could do that why do the bias thing? Just find the best resumes using machine learning. Way better output product.
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Assumption is that if the whole hiring / funding committee is white dudes, the same "best" resume won't be equal if applicant is black woman vs. white male (for example).
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I don't have the strongest opinion on how prevalent prejudice is in hiring, but I have read that it's not white dudes. It's women.
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