Lauren R. McCarey
ABSTRACT
There are many cases where one may wish to retrieve non-random, diverse, and fair samples from an imbalanced dataset. With over 90K tech job descriptions and corresponding resumes that applied to those jobs, we describe our approach using evolutionary algorithms to derive a diverse and gender-fair subset for use in validating ML algorithms. Since 3/4 of the applicants were male, we had an imbalanced dataset. We describe how, through the use of evolutionary algorithms, we were able to discover different characteristics between genders as well as recognize issues with sparse representations. We constructed additional optimizing objectives to rectify these issues to ultimately unearth a desired sample.
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Index Terms
- Optimizing sample diversity with fairness constraints on imbalanced, sparse, hiring data
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