There is a growing demand for ML methods that limit inappropriate use of protected information to avoid both disparate treatment and disparate impact. In this talk, we present Generative Adversarial rePresentations (GAP) as a data-driven framework that leverages recent advancements in adversarial learning to allow a data holder to learn universal representations that decouple a set of sensitive attributes from the rest of the dataset while allowing learning multiple downstream tasks. We will briefly highlight the theoretical and practical results of GAP.

In the second half of the talk we will focus on model auditing. Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, models (even those learned with privacy guarantees) can inadvertently memorize unique training examples or leak sensitive features. To identify such privacy violations, existing model auditing techniques use finite adversaries defined as machine learning models with (a) access to some finite side information (e.g., a small auditing dataset), and (b) finite capacity (e.g., a fixed neural network architecture). In the second half of the talk, we present requirements under which an unsuccessful attempt to identify privacy violations by a finite adversary implies that no stronger adversary can succeed at such a task. We will do so via parameters that quantify the capabilities of the finite adversary, including the size of the neural network employed by such an adversary and the amount of side information it has access to as well as the regularity of the (perhaps privacy-guaranteeing) audited model.