26 citations · 71 across the 12 of their papers we have counts for
12 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,…
A Taxonomy of Challenges to Curating Fair Datasets
Dora Zhao, Morgan Klaus Scheuerman, Pooja Chitre +5
Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation. Drawing from intervie…
Augmented Datasheets for Speech Datasets and Ethical Decision-Making
Orestis Papakyriakopoulos, Anna Seo Gyeong Choi, Jerone Andrews +5
Speech datasets are crucial for training Speech Language Technologies (SLT); however, the lack of diversity of the underlying training data can lead to serious limitations in build…
A View From Somewhere: Human-Centric Face Representations
Jerone T. A. Andrews, Przemyslaw Joniak, Alice Xiang
Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categ…
Ethical Considerations for Responsible Data Curation
Jerone T. A. Andrews, Dora Zhao, William Thong +3
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed th…
Men Also Do Laundry: Multi-Attribute Bias Amplification
Dora Zhao, Jerone T. A. Andrews, Alice Xiang
As computer vision systems become more widely deployed, there is increasing concern from both the research community and the public that these systems are not only reproducing but…
I call BS: Fraud Detection in Crowdfunding Campaigns
Beatrice Perez, Sara R. Machado, Jerone T. A. Andrews +1
Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but hav…
Conditional Adversarial Camera Model Anonymization
Jerone T. A. Andrews, Yidan Zhang, Lewis D. Griffin
The model of camera that was used to capture a particular photographic image (model attribution) is typically inferred from high-frequency model-specific artifacts present within t…
Multiple-Identity Image Attacks Against Face-based Identity Verification
Jerone T. A. Andrews, Thomas Tanay, Lewis D. Griffin
Facial verification systems are vulnerable to poisoning attacks that make use of multiple-identity images (MIIs)---face images stored in a database that resemble multiple persons,…
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations
Thomas Tanay, Jerone T. A. Andrews, Lewis D. Griffin
Designing models that are robust to small adversarial perturbations of their inputs has proven remarkably difficult. In this work we show that the reverse problem---making models m…