  Machine learning (ML) models are increasingly used for high-stake
applications that can greatly impact people's lives. Despite their use, these
models have the potential to be biased towards certain social groups on the
basis of race, gender, or ethnicity. Many prior works have attempted to
mitigate this "model discrimination" by updating the training data
(pre-processing), altering the model learning process (in-processing), or
manipulating model output (post-processing). However, these works have not yet
been extended to the realm of multi-sensitive parameters and sensitive options
(MSPSO), where sensitive parameters are attributes that can be discriminated
against (e.g race) and sensitive options are options within sensitive
parameters (e.g black or white), thus giving them limited real-world usability.
Prior work in fairness has also suffered from an accuracy-fairness tradeoff
that prevents both the accuracy and fairness from being high. Moreover,
previous literature has failed to provide holistic fairness metrics that work
with MSPSO. In this paper, we solve all three of these problems by (a) creating
a novel bias mitigation technique called DualFair and (b) developing a new
fairness metric (i.e. AWI) that can handle MSPSO. Lastly, we test our novel
mitigation method using a comprehensive U.S mortgage lending dataset and show
that our classifier, or fair loan predictor, obtains better fairness and
accuracy metrics than current state-of-the-art models.
