Mitigating Unwanted Biases with Adversarial Learning

Understand

Machine learning is a tool for building models that accurately represent input training data.

• When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases.
• We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary.
• The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code.