16 citations · 49 across the 20 of their papers we have counts for
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Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets
Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu +1
Many existing works have made great strides towards reducing racial bias in face recognition. However, most of these methods attempt to rectify bias that manifests in models during…
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
Matthew Gwilliam, Adam Teuscher, Connor Anderson +1
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their p…
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The ampl…