I was building collections models for Revenue Canada (CRA) to help them prioritze collections for taxpayers who owed money on their personal income taxes. To improve prediction accuracy, we used neural networks rather than simpler (and arguably more interpretable) statistical models like regression. But, of course, neural networks are very difficult to explain: their complexity, a virtue for accuracy, becomes a liability for explainability. Moreover, the CRA needed to provide justification for why taxpayers received high risk scores. The analysis that worked for them was to use an interpretable model to explain the neural network. I built a decision tree to predict the neural network predictions (this is the key--it didn't predict the a ctual target variable, but rather the neural network's estimate of the actual target variable). This tree was able to provide the gist of what the neural network was doing, but in rules rather than equations, which are far easier for stakeholders to understand. The model with the tree interpretation was accepted.
In machine learning, balancing interpretability and model complexity is a typical difficulty, especially when deciding between complex algorithms and more straightforward, interpretable models. Here is a case study: Scenario: Evaluating the Risk of Loan Default Context: A FinTech Company needs to forecast the possibility of loan defaults. The goal is to minimize risk while ensuring regulatory compliance, which requires that the model's decisions be explainable. Complex Models: Gradient Boosting Machines (GBM) Neural Networks Interpretable Models: Logistic Regression Decision Trees Balancing considerations Accuracy: Because complex models like GBM and neural networks can capture nonlinear correlations and interactions between features, they usually offer higher predicted accuracy. Interpretability: The model's predictions must be easily understood by non-technical stakeholders, such as customers and regulators, in order to be in compliance with regulations. Because of their transparency, decision trees and logistic regression are recommended. Regulatory Compliance: Transparency is essential because financial regulations frequently demand that company explain any choice they make. This is in favor of simpler models with an obvious relationship between inputs and outputs. Handle with a Hybrid Approach Initial Screening with a Complex Model: To find important features and intricate relationships in the data, use GBM or neural networks to screen for patterns and interactions. These models can be used as standards to determine the possible level of accuracy and insights. Interpretable Model for Decision Making: Create a decision tree and logistic regression model based on sophisticated model insights. Prioritize interpretability while ensuring model accuracy. L1 regularization (lasso) in logistic regression is used to simplify the model even further. Beyond the hybrid model method, other approaches can be taken, such as Rule-Based Models, Explainable AI (XAI) Techniques, and Model Compression Techniques. To maintain regulatory compliance, and stakeholder confidence, and balance the model's complexity and interpretability without materially sacrificing its predictive performance. An expert solution may be achieved by combining sophisticated models for investigation with understandable models for decision-making.