  Algorithmic fairness, the research field of making machine learning (ML)
algorithms fair, is an established area in ML. As ML technologies expand their
application domains, including ones with high societal impact, it becomes
essential to take fairness into consideration during the building of ML
systems. Yet, despite its wide range of socially sensitive applications, most
work treats the issue of algorithmic bias as an intrinsic property of
supervised learning, i.e., the class label is given as a precondition. Unlike
prior studies in fairness, we propose an individual fairness measure and a
corresponding algorithm that deal with the challenges of uncertainty arising
from censorship in class labels, while enforcing similar individuals to be
treated similarly from a ranking perspective, free of the Lipschitz condition
in the conventional individual fairness definition. We argue that this
perspective represents a more realistic model of fairness research for
real-world application deployment and show how learning with such a relaxed
precondition draws new insights that better explains algorithmic fairness. We
conducted experiments on four real-world datasets to evaluate our proposed
method compared to other fairness models, demonstrating its superiority in
minimizing discrimination while maintaining predictive performance with
uncertainty present.
