Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
This addresses fairness in machine learning for scenarios like recommender systems where sensitive attributes are unknown, though it appears incremental in its approach.
The paper tackles the problem of learning fair classifiers without access to sensitive attributes during training or serving, using adversarial training to remove such information from latent representations. It finds that only a small amount of data is needed for training and that the data distribution empirically shapes the adversary's fairness notion.
How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group? How can we train such a classifier when data on the protected group is difficult to attain? In many settings, finding out the sensitive input attribute can be prohibitively expensive even during model training, and sometimes impossible during model serving. For example, in recommender systems, if we want to predict if a user will click on a given recommendation, we often do not know many attributes of the user, e.g., race or age, and many attributes of the content are hard to determine, e.g., the language or topic. Thus, it is not feasible to use a different classifier calibrated based on knowledge of the sensitive attribute. Here, we use an adversarial training procedure to remove information about the sensitive attribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training effects the resulting fairness properties. We find two interesting results: a small amount of data is needed to train these adversarial models, and the data distribution empirically drives the adversary's notion of fairness.