  We present RAWLSNET, a system for altering Bayesian Network (BN) models to
satisfy the Rawlsian principle of fair equality of opportunity (FEO).
RAWLSNET's BN models generate aspirational data distributions: data generated
to reflect an ideally fair, FEO-satisfying society. FEO states that everyone
with the same talent and willingness to use it should have the same chance of
achieving advantageous social positions (e.g., employment), regardless of their
background circumstances (e.g., socioeconomic status). Satisfying FEO requires
alterations to social structures such as school assignments. Our paper
describes RAWLSNET, a method which takes as input a BN representation of an FEO
application and alters the BN's parameters so as to satisfy FEO when possible,
and minimize deviation from FEO otherwise. We also offer guidance for applying
RAWLSNET, including on recognizing proper applications of FEO. We demonstrate
the use of our system with publicly available data sets. RAWLSNET's altered BNs
offer the novel capability of generating aspirational data for FEO-relevant
tasks. Aspirational data are free from the biases of real-world data, and thus
are useful for recognizing and detecting sources of unfairness in machine
learning algorithms besides biased data.
