  In the past few years, Artificial Intelligence (AI) has garnered attention
from various industries including financial services (FS). AI has made a
positive impact in financial services by enhancing productivity and improving
risk management. While AI can offer efficient solutions, it has the potential
to bring unintended consequences. One such consequence is the pronounced effect
of AI-related unfairness and attendant fairness-related harms. These
fairness-related harms could involve differential treatment of individuals; for
example, unfairly denying a loan to certain individuals or groups of
individuals. In this paper, we focus on identifying and mitigating individual
unfairness and leveraging some of the recently published techniques in this
domain, especially as applicable to the credit adjudication use case. We also
investigate the extent to which techniques for achieving individual fairness
are effective at achieving group fairness. Our main contribution in this work
is functionalizing a two-step training process which involves learning a fair
similarity metric from a group sense using a small portion of the raw data and
training an individually "fair" classifier using the rest of the data where the
sensitive features are excluded. The key characteristic of this two-step
technique is related to its flexibility, i.e., the fair metric obtained in the
first step can be used with any other individual fairness algorithms in the
second step. Furthermore, we developed a second metric (distinct from the fair
similarity metric) to determine how fairly a model is treating similar
individuals. We use this metric to compare a "fair" model against its baseline
model in terms of their individual fairness value. Finally, some experimental
results corresponding to the individual unfairness mitigation techniques are
presented.
