  This paper investigates the application of machine learning when training a
credit decision model over real, publicly available data whilst accounting for
"bias objectives". We use the term "bias objective" to describe the requirement
that a trained model displays discriminatory bias against a given groups of
individuals that doesn't exceed a prescribed level, where such level can be
zero. This research presents an empirical study examining the tension between
competing model training objectives which in all cases include one or more bias
objectives.
  This work is motivated by the observation that the parties associated with
creditworthiness models have requirements that can not certainly be fully met
simultaneously. The research herein seeks to highlight the impracticality of
satisfying all parties' objectives, demonstrating the need for "trade-offs" to
be made. The results and conclusions presented by this paper are of particular
importance for all stakeholders within the credit scoring industry that rely
upon artificial intelligence (AI) models as part of the decision-making process
when determining the creditworthiness of individuals. This paper provides an
exposition of the difficulty of training AI models that are able to
simultaneously satisfy multiple bias objectives whilst maintaining acceptable
levels of accuracy. Stakeholders should be aware of this difficulty and should
acknowledge that some degree of discriminatory bias, across a number of
protected characteristics and formulations of bias, cannot be avoided.
