A Declarative Approach to Fairness in Relational Domains

TitleA Declarative Approach to Fairness in Relational Domains
Publication TypeJournal Article
Year of Publication2019
AuthorsFarnadi, G, Babaki, B, Getoor, L
JournalIEEE Data Engineering Bulletin
Volume42
Issue3
Pagination36--48
AbstractAI 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 financial qualifications. 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. Furthermore, we extend an existing statistical relational learning framework, probabilistic soft logic (PSL), to incorporate our definition of relational fairness. We refer to this fairness-aware framework FairPSL. FairPSL makes use of the logical definitions of fairnesss but also supports a probabilistic interpretation. In particular, we show how to perform maximum a posteriori (MAP) inference by exploiting probabilistic dependencies within the domain while avoiding violations of fairness guarantees. Preliminary empirical evaluation shows that we are able to make both accurate and fair decisions.