## 2 Background
### Related Work  
As we have previously introduced, new regulations in CFD have made the auditing and verifiability of decisions mandatory, thereby increasing the demand for the ability to question, understand, and trust ML systems. Consequently, interpretability has become essential to breaking the barrier that is the lack of transparency in ML. The following section presents a summary of relevant works related to these limitations. ML techniques have a large number of parameters and settings that make them very complex systems. This can often mean that the user of the model is not always able to grasp what knowledge the model has learned from the data so as to make the final decision, thereby leading sometimes to the user's distrust of these models [8]. In this context, the main challenges for ML applications in CFD are a lack of interpretability and biases (this last one both in data and algorithms) [7]. The first challenge in ML interpretability is the interpretation of the reasons behind a model decision's in a way that a human can understand [8]. In financial services, firms have great challenges in meeting regulatory requirements to explain decision making when using black-box models. The regulation should not be considered as an unjustified barrier to ML deployment, but some firms emphasize the need for additional guidance on how to interpret current regulations [7]. Current works rely on naturally interpretable models,for instance, linear models [11]. With models of this kind, it is easy to understand the predictions by just scrutinizing the weights for each feature in the model. Other relevant work [14] is focused on local surrogate models, such as local interpretable model-agnostic explanations (LIME). Said surrogate models are trained to approximate the predictions of the underlying black-box models. Instead of training a global surrogate model, LIME focuses on training local models to explain individual predictions. A model with 100% accuracy cannot be used if the decision process cannot be explained. In this context, some interesting works have started questioning the cost of interpretability and how this interpretability affects predictive accuracy [15]. The next challenge in black boxes consequences is biases. ML algorithms in many cases operate by seeking correlations that try to maximize the predictive power. However, the use of ML in financial services also raises several potential problems, given that the data used to train the ML algorithms may not be representative data for the problem [9]. In some cases, this can produce results based on spurious relationships and thus lead to biased conclusions being drawn [6]. In spite of training these models with enough data, the algorithms can become more ingrained, and previously unforeseen risks are appearing, including the risk that a perfectly well-intentioned algorithm may inadvertently generate biased conclusions that discriminate against protected classes of people [16]. Algorithms often do not distinguish causation from correlation, or they do not know when it is necessary to gather additional data to form a sound conclusion, and for those reasons it is crucial to obtain interpretability for ML models applied to financial services. Interesting works are currently underway that are developing techniques to detect the biases of these algorithms [14; 15; 17]. In order to improve the accuracy in ML algorithms and to provide interpretability while reducing biases, the so-called feature selection stage is crucial. As the data multiply, the quality of data required for processing ML algorithms decreases gradually, an effect that has long been known as the curse of dimensionality [18; 19]. Higher dimensional data lead to the existence of noisy, irrelevant, and redundant data, which in turn cause overfitting of the model and increase the error rate of the learning algorithm. To handle these problems, techniques of dimensionality reduction can be applied.