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

33 citations · 40 across the 2 of their papers we have counts for

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2 papers

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…

cs.CL2021★ 7 cited

Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets

Kofi Arhin, Ioana Baldini, Dennis Wei +2

The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are…