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Found 3 papers, 1 papers with code
Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A Case Study of Automated Video Interviews
We provide a psychometric-grounded exposition of bias and fairness as applied to a typical machine learning pipeline for affective computing.
Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy
In this study, we systematically under- and oversampled minority (Black and Hispanic) applicants to manipulate adverse impact ratios in training data and investigated how training data adverse impact ratios affect ML model adverse impact and accuracy.
Studying Politeness across Cultures Using English Twitter and Mandarin Weibo
We study the linguistic features associated with politeness across US English and Mandarin Chinese.
Social and Information Networks Computers and Society
