  Data-driven decision making related to individuals has become increasingly
pervasive, but the issue concerning the potential discrimination has been
raised by recent studies. In response, researchers have made efforts to propose
and implement fairness measures and algorithms, but those efforts have not been
translated to the real-world practice of data-driven decision making. As such,
there is still an urgent need to create a viable tool to facilitate fair
decision making. We propose FairSight, a visual analytic system to address this
need; it is designed to achieve different notions of fairness in ranking
decisions through identifying the required actions -- understanding, measuring,
diagnosing and mitigating biases -- that together lead to fairer decision
making. Through a case study and user study, we demonstrate that the proposed
visual analytic and diagnostic modules in the system are effective in
understanding the fairness-aware decision pipeline and obtaining more fair
outcomes.
