A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairness

• URL: http://arxiv.org/abs/2101.02831v1
• Date: Fri, 8 Jan 2021 03:13:44 GMT
• Title: A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairness
• Authors: Becky Mashaido and Winston Moh Tangongho
• Abstract summary: Motivated by the need for fair algorithmic decision making in the age of automation and artificially-intelligent technology, this technical report provides a theoretical insight into adversarial training for fairness in deep learning. We build upon previous work in adversarial fairness, show the persistent tradeoff between fair predictions and model performance, and explore further mechanisms that help in offsetting this tradeoff.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by the need for fair algorithmic decision making in the age of automation and artificially-intelligent technology, this technical report provides a theoretical insight into adversarial training for fairness in deep learning. We build upon previous work in adversarial fairness, show the persistent tradeoff between fair predictions and model performance, and explore further mechanisms that help in offsetting this tradeoff.

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