What are some strategies for ensuring your machine learning models are interpretable by regulators?

1 Why interpretability matters

Interpretability is the ability to explain how a machine learning model makes decisions, predictions, or recommendations. It is important for regulators because it helps them assess the fairness, accuracy, robustness, and accountability of your models. For example, if your model denies a loan application, you need to be able to justify why and how it did so, and whether it considered any protected attributes, such as race, gender, or age. Interpretability also helps you build trust and transparency with your customers, who may want to know how your model affects their financial outcomes.

2 Choose the right model

One strategy for ensuring interpretability is to choose the right model for your FinTech problem. Some models are inherently more interpretable than others, depending on their complexity, structure, and assumptions. For example, linear regression, decision trees, and rule-based models are generally easier to understand and explain than deep neural networks, random forests, or support vector machines. However, this does not mean you have to sacrifice performance for interpretability. You can use techniques such as feature selection, regularization, pruning, or simplification to reduce the complexity and improve the clarity of your models.

3 Use explainability methods

Another strategy for ensuring interpretability is to use explainability methods, which are tools and techniques that help you understand and communicate how your models work. There are two main types of explainability methods: global and local. Global methods provide an overview of how your model behaves across all data points, such as feature importance, coefficients, or rules. Local methods provide insights into how your model behaves for specific data points, such as individual predictions, counterfactuals, or influence functions. You can use a combination of global and local methods to provide a comprehensive and detailed explanation of your model to regulators.

4 Document and validate your model

A third strategy for ensuring interpretability is to document and validate your model, which means keeping a record of how you developed, tested, and deployed your model, and how you monitor and update it. Documentation helps you demonstrate the rationale, logic, and evidence behind your model, as well as the assumptions, limitations, and risks involved. Validation helps you verify the accuracy, reliability, and consistency of your model, as well as the compliance with regulatory standards and ethical principles. You can use frameworks such as AI Explainability 360, LIME, or SHAP to document and validate your model.

5 Engage with regulators

A final strategy for ensuring interpretability is to engage with regulators, which means communicating and collaborating with them throughout the model lifecycle. Engagement helps you understand the expectations, requirements, and feedback of regulators, as well as the best practices and guidelines for interpretability. It also helps you establish a dialogue and a relationship with regulators, which can facilitate the approval and review process of your model. You can use channels such as workshops, reports, audits, or consultations to engage with regulators.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?