FairTargetSim: An Interactive Simulator for Understanding and Explaining
  the Fairness Effects of Target Variable Definition
Dalia Gala Milo Phillips-Brown Naman Goel Carinal Prunkl Laura Alvarez Jubete medb corcoran Ray Eitel-Porter
Machine learning requires defining one's target variable for predictions or
decisions, a process that can have profound implications on fairness: biases
are often encoded in target variable definition itself, before any data
collection or training. We present an interactive simulator, FairTargetSim
(FTS), that illustrates how target variable definition impacts fairness. FTS is
a valuable tool for algorithm developers, researchers, and non-technical
stakeholders. FTS uses a case study of algorithmic hiring, using real-world
data and user-defined target variables. FTS is open-source and available at:
http://tinyurl.com/ftsinterface. The video accompanying this paper is here:
http://tinyurl.com/ijcaifts.