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Found 4 papers, 3 papers with code
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models
A few large, homogenous, pre-trained models undergird many machine learning systems — and often, these models contain harmful stereotypes learned from the internet.
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition.
Heuristic-Based Weak Learning for Automated Decision-Making
Machine learning systems impact many stakeholders and groups of users, often disparately.
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
We shed light on the ways in which appearance bias could be embedded in face processing technology and cast further doubt on the practice of predicting subjective traits based on appearances.
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