Statistical Equity: A Fairness Classification Objective

Understand

Machine learning systems have been shown to propagate the societal errors of the past.

• In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent.
• We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases.
• We formalize our definition of fairness, and motivate it with its appropriate contexts.