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.

翻译：机器学习需要对预测或决策的目标变量进行定义，这一过程可能对公平性产生深远影响：在数据收集或模型训练之前，偏见往往已编码于目标变量本身的定义中。我们提出交互式模拟器FairTargetSim（FTS），展示了目标变量定义如何影响公平性。FTS是面向算法开发者、研究人员及非技术利益相关者的重要工具。该模拟器以算法招聘为案例研究，使用真实数据与用户自定义目标变量。FTS为开源项目，可通过 http://tinyurl.com/ftsinterface 获取，配套论文视频见 http://tinyurl.com/ijcaifts。