Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects

Toshihiro Kamishima

Toshihiro Kamishima, Kazuto Fukuchi, Jun Sakuma, Shotaro Akaho, and Hideki Asoh

The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:

  1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.

  2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.

  3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.

Slides

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects from Toshihiro Kamishima