Model Interpretability in Machine Learning

Algorithm Power at LogicPlumWhat is Model Interpretability in Machine Learning? #

Model interpretability is how well humans can understand the machine learning algorithm’s decision-making process.

Historically, artificial intelligence was considered a black box – something that worked, but data scientists had no explanation or insights about how the inner processes arrived at the predictions. In other words, a black-box model is inherently uninterpretable.

This makes it challenging to explain outputs to decision-makers and regulatory agencies, reducing their trust in machine learning models.

There are three primary levels of model interpretability: global, local, per feature. Global interpretation explains the entire model as a whole, while local explanations focus on understanding individual outcomes. The per feature interpretability works to clarify how a feature interacts with the model’s predictions or the target variable.

Specific artificial intelligence platforms have improved interpretability, such as logistic regression models. These are often straightforward and can be easily explained to the end-user, but they can become complex depending on the number of features involved.

Deep learning algorithms are far more complicated – explaining the predictions from these models can be quite tricky but improving model interpretability is essential for businesses to make data-driven decisions.

Why Is Model Interpretability Important? #

Understanding why a model predicted an outcome, or model interpretability, is essential when businesses rely on the algorithm to make strategic decisions. If the machine learning model cannot be explained to regulators within the context of compliance, the organization may not even utilize it.

Let’s consider an example where a bank uses an artificial intelligence platform to make decisions regarding whether or not to extend a loan to a customer. Legally the customer has a right to know what factors were used to determine the approval or denial, and regulators will need to see this documentation.

If the bank does not have a clear understanding of how the model decided to approve or deny credit, they will not use it to help them make these decisions.

The level of model interpretability requirements will vary depending on the types of decisions the predictions are being used to make, but in most cases, increased transparency is a good thing.

Model Interpretability + LogicPlum #

At LogicPlum, we want you to utilize our top-tier machine learning models to make essential business decisions, so we will provide you with model blueprints that explain the steps the algorithms used to arrive at the predictions. We will even work with you to help explain these models to regulatory agencies so that you can maximize the benefit of using AI in your business.

Our prediction explanations will also identify the features that had the most impact on the model’s outcomes, giving you better insights into what drives your customers and industry.