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

http://www.slideshare.net/shima__shima/future-directions-of-fairnessaware-data-mining-recommendation-causality-and-theoretical-aspects