most citedOversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy

33 citations · 66 across the 3 of their papers we have counts for

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cs.LG2023★ 22 cited

Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A Case Study of Automated Video Interviews

Brandon M Booth, Louis Hickman, Shree Krishna Subburaj +3

We provide a psychometric-grounded exposition of bias and fairness as applied to a typical machine learning pipeline for affective computing. We expand on an interpersonal communic…

cs.LG2023★ 33 cited

Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy

Louis Hickman, Jason Kuruzovich, Vincent Ng +2

Organizations are increasingly adopting machine learning (ML) for personnel assessment. However, concerns exist about fairness in designing and implementing ML assessments. Supervi…