26 citations · 52 across the 9 of their papers we have counts for
9 papers
Efficient Bias Mitigation Without Privileged Information
Mateo Espinosa Zarlenga, Swami Sankaranarayanan, Jerone T. A. Andrews +3
Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously…
Position: Measure Dataset Diversity, Don't Just Claim It
Dora Zhao, Jerone T. A. Andrews, Orestis Papakyriakopoulos +1
Machine learning (ML) datasets, often perceived as neutral, inherently encapsulate abstract and disputed social constructs. Dataset curators frequently employ value-laden terms suc…
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao +4
We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes,…
Estimating the Likelihood of Arrest from Police Records in Presence of Unreported Crimes
Riccardo Fogliato, Arun Kumar Kuchibhotla, Zachary Lipton +3
Many important policy decisions concerning policing hinge on our understanding of how likely various criminal offenses are to result in arrests. Since many crimes are never reporte…
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
William Thong, Przemyslaw Joniak, Alice Xiang
This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have sh…
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
Keziah Naggita, Julienne LaChance, Alice Xiang
Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standar…