565 citations · 837 across the 31 of their papers we have counts for
6 papers · 1 filter
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,…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
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…
Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators
Wiebke Hutiri, Oresiti Papakyriakopoulos, Alice Xiang
The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing…