Learning Social Fairness Preferences from Non-Expert Stakeholder
  Opinions in Kidney Placement
Mukund Telukunta Sukruth Rao Gabriella Stickney Venkata Sriram Siddardh Nadendla Casey Canfield
Modern kidney placement incorporates several intelligent recommendation
systems which exhibit social discrimination due to biases inherited from
training data. Although initial attempts were made in the literature to study
algorithmic fairness in kidney placement, these methods replace true outcomes
with surgeons' decisions due to the long delays involved in recording such
outcomes reliably. However, the replacement of true outcomes with surgeons'
decisions disregards expert stakeholders' biases as well as social opinions of
other stakeholders who do not possess medical expertise. This paper alleviates
the latter concern and designs a novel fairness feedback survey to evaluate an
acceptance rate predictor (ARP) that predicts a kidney's acceptance rate in a
given kidney-match pair. The survey is launched on Prolific, a crowdsourcing
platform, and public opinions are collected from 85 anonymous crowd
participants. A novel social fairness preference learning algorithm is proposed
based on minimizing social feedback regret computed using a novel logit-based
fairness feedback model. The proposed model and learning algorithm are both
validated using simulation experiments as well as Prolific data. Public
preferences towards group fairness notions in the context of kidney placement
have been estimated and discussed in detail. The specific ARP tested in the
Prolific survey has been deemed fair by the participants.