Fairness-Aware Relational Learning and Inference

AI and machine learning tools are being used with increasing frequency for decision making in domains that affect peoples' lives such as employment, education, policing and loan approval. These uses raise concerns about biases of algorithmic discrimination and have motivated the development of fairness-aware machine learning. However, existing fairness approaches are based solely on attributes of individuals. In many cases, discrimination is much more complex, and taking into account the social, organizational, and other connections between individuals is important. We introduce new notions of fairness that are able to capture the relational structure in a domain. We use first-order logic to provide a flexible and expressive language for specifying complex relational patterns of discrimination. We incorporate our definition of relational fairness to propose 1) fairness-aware constrained conditional inference subject to common data-oriented fairness measures and 2) fairness-aware parameter learning by incorporating decision-oriented fairness measures.