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:
Recently, Beutel et. al. REF leveraged adversarial training procedures to remove information about sensitive group membership from the latent representations learned by a neural network.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
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Computer Science