One-Network Adversarial Fairness


  • Tameem Adel University of Cambridge
  • Isabel Valera MPI-IS
  • Zoubin Ghahramani University of Cambridge
  • Adrian Weller Cambridge University



There is currently a great expansion of the impact of machine learning algorithms on our lives, prompting the need for objectives other than pure performance, including fairness. Fairness here means that the outcome of an automated decisionmaking system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy. Our framework provides one way to quantify the tradeoff between fairness and accuracy, while also leading to strong empirical performance.




How to Cite

Adel, T., Valera, I., Ghahramani, Z., & Weller, A. (2019). One-Network Adversarial Fairness. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2412-2420.



AAAI Technical Track: Human-AI Collaboration