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Found 4 papers, 1 papers with code
Quantifying and mitigating the impact of label errors on model disparity metrics
We empirically assess the proposed approach on a variety of datasets and find significant improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric.
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
These metrics successfully incentivized performance improvements on person-centric tasks such as face analysis and are used to understand risks of modern models.
Adaptive Sampling Strategies to Construct Equitable Training Datasets
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 traditionally underserved groups.
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
We discuss how we disentangle normative questions of product and policy design (like, "how should the system trade off between different stakeholders' interests and needs?")
