Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative impacts on the lives of these people. Therefore, eliminating bias and ensuring fairness within these models is crucial to the societal expectations these institutions would like to meet. While there are tools and research towards technical mitigation methods for bias and unfairness. There is a lack of focus on the process of implementing these tools and methods within the industry, to develop ML models with the consideration of fairness at an early stage. In particular, there is a lack of specification on what fairness goals and objectives the ML model should accomplish. Without having this clarity, industry stakeholders can apply the tools and algorithmic unfairness mitigation methods but if the fairness requirements are not defined, then a) one is still not sure whether the ML model is solving the correct fairness goals and b) one is still not sure what the trade-offs and feasibility within different fairness goals, and available resources may look like. To address this, we design a Multi-Level Fairness Framework (digital workflow) that aims towards supporting stakeholders within the industry to perform Requirements Engineering, specifically elicitation and modeling, for fairness in a Machine Learning Model.

In recent years financial fraud has seen substantial growth due to the advent of electronic financial services opening many doors for fraudsters. Consequently, the industry of fraud detection has seen a significant growth in scale, but moves slowly in comparison to the ever-changing nature of fraudulent behavior. As the monetary losses associated with financial fraud continue to grow, so does the need for efficient automated decision making systems. Simple decision making rules are often still the industry standard and only show decent results in the short-term, as reverse-engineering such rules is an easy task for smart fraudsters. Supervised learning systems as automated fraud detectors have shown promising results across the field, but are plagued by challenges uniquely prevalent in the field. Disproportional class imbalance in fraudulent transactions, as well as fraudsters continually adopting new schemes make training robust and generally applicable machine learning models an arduous task. This work introduces a novel machine learning pipeline, which makes use of carefully selected synthetic samples of this minority class to augment the training dataset of the supervised model. Synthetic samples representing fraudulent transactions are filtered based on a novel technique to quantify their expected performance as an adversarial example, using both data-driven and human-expert-driven techniques. By providing the supervised model with high-quality synthetic adversarial examples, we aim to improve its generalizability to never-seen-before fraudulent behavior and, in turn, improve its robustness to the volatile nature of financial fraud. Our results show that weakly-supervised models trained on our augmented datasets are able to detect 7% more fraudulent transactions compared to a baseline model trained on the standard dataset, at the cost of a 1% increase in false positives. Our calculations further show that applying this system could lead to a decrease of 1/6 in monetary losses incurred by financial fraud.