Fairness in Relational Domains

By G. Farnadi, Behrouz Babaki, Lise Getoor. 2018

35 citations recorded by the source corpus.

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. 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 violation of fairness guarantees. Preliminary empirical evaluation shows that we are able to make both accurate and fair decisions.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dan Suciu
2. Babak Salimi
3. Bill Howe
4. Luke Rodriguez

In Safety Research

1. Dan Suciu
2. Babak Salimi
3. Bill Howe
4. Luke Rodriguez

In Sociology and Political Science

1. Dan Suciu
2. Babak Salimi
3. Bill Howe
4. Luke Rodriguez

In AI Ethics, Fairness & Society

1. Dan Suciu
2. Babak Salimi
3. Bill Howe
4. Luke Rodriguez

Citations in the corpus

• Interventional Fairness: Causal Database Repair for Algorithmic Fairness (2019, background): “Recently, associational fairness has bee studied in the context of statistical relational learning [14, 15].”