  Any decision, such as one about who to hire, involves two components. First,
a rational component, i.e., they have a good education, they speak clearly.
Second, an affective component, based on observables such as visual features of
race and gender, and possibly biased by stereotypes. Here we propose a method
for measuring the affective, socially biased, component, thus enabling its
removal. That is, given a decision-making process, these affective measurements
remove the affective bias in the decision, rendering it fair across a set of
categories defined by the method itself. We thus propose that this may solve
three key problems in intersectional fairness: (1) the definition of categories
over which fairness is a consideration; (2) an infinite regress into smaller
and smaller groups; and (3) ensuring a fair distribution based on basic human
rights or other prior information. The primary idea in this paper is that
fairness biases can be measured using affective coherence, and that this can be
used to normalize outcome mappings. We aim for this conceptual work to expose a
novel method for handling fairness problems that uses emotional coherence as an
independent measure of bias that goes beyond statistical parity.
