  Bias in machine learning has manifested injustice in several areas, such as
medicine, hiring, and criminal justice. In response, computer scientists have
developed myriad definitions of fairness to correct this bias in fielded
algorithms. While some definitions are based on established legal and ethical
norms, others are largely mathematical. It is unclear whether the general
public agrees with these fairness definitions, and perhaps more importantly,
whether they understand these definitions. We take initial steps toward
bridging this gap between ML researchers and the public, by addressing the
question: does a lay audience understand a basic definition of ML fairness? We
develop a metric to measure comprehension of three such
definitions--demographic parity, equal opportunity, and equalized odds. We
evaluate this metric using an online survey, and investigate the relationship
between comprehension and sentiment, demographics, and the definition itself.
