4 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 1 cited
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Cody Blakeney, Gentry Atkinson, Nathaniel Huish +3
Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discriminatio…
cs.LG2021★ 4 cited
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation
Cody Blakeney, Nathaniel Huish, Yan Yan +1
In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or disc…