How can we learn a classi er that is "fair" for a protected or sensitive group, when we do not know if the input to the classi er belongs to the protected group? How can we train such a classi er when data on the protected group is di cult to a ain? In many settings, nding out the sensitive input a ribute 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 o en do not know many a ributes of the user, e.g., race or age, and many a ributes of the content are hard to determine, e.g., the language or topic. us, it is not feasible to use a di erent classi er calibrated based on knowledge of the sensitive a ribute. Here, we use an adversarial training procedure to remove information about the sensitive a ribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training e ects the resulting fairness properties. We nd 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. ACM Reference format:
The work in REF analyzes the impact of the data distribution on the fairness notion adopted by the adversary.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
ArXiv
ArXiv
Computer Science