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A Critical Review of Predominant Bias in Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predominant biases is crucial in advanci…
DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
Jiageng Zhu, Hanchen Xie, Jiazhi Li +1
Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various d…
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising meth…
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising met…