273 citations · 332 across the 5 of their papers we have counts for
5 papers
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
Adaptive Sampling Strategies to Construct Equitable Training Datasets
William Cai, Ro Encarnacion, Bobbie Chern +4
In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of…
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
Chloé Bakalar, Renata Barreto, Stevie Bergman +13
Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world…
Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination
Miranda Bogen, Aaron Rieke, Shazeda Ahmed
Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, s…
Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
Muhammad Ali, Piotr Sapiezynski, Miranda Bogen +3
The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many w…