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Found 3 papers, 1 papers with code
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name.
Entity Personalized Talent Search Models with Tree Interaction Features
Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives.
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