Exploring discrimination: A user-centric evaluation of discrimination-aware data mining

Discrimination-aware data mining (DADM) aims at deriving patterns that do not discriminate on "unjust grounds" such as gender, ethnicity or nationality. DADM safeguards can be very helpful for decision-support applications in fields such as banking or employment. However, constraining data mining to exclude a fixed enumeration of potentially discriminatory features is too restrictive. It should be complemented by exploratory DADM. We discuss these two forms of DADM and their requirements for evaluation, and we discuss and refine our DCUBE-GUI tool as a system for exploratory DADM. In a user study administered via Mechanical Turk, we show that tools such as DCUBE-GUI can successfully assist novice users in exploring discrimination in data mining.