DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

Understand

Machine learning models have been criticized for reflecting unfair biases in the training data.

• Instead of solving for this by introducing fair learning algorithms directly, we focus on generating fair synthetic data, such that any downstream learner is fair.
• Generating fair synthetic data from unfair data - while remaining truthful to the underlying data-generating process (DGP) - is non-trivial.
• In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data.