Achieving Explainability in Machine Learning: Case Studies

Achieving Explainability in Machine Learning: Case Studies

Table of Contents:

I. Introduction II. The Importance of Explainability in Machine Learning III. Case Study 1: Fair Lending A. Logistic Regression B. Boosted Trees C. Kernel SHAP IV. Case Study 2: Medicine A. Generalized Additive Models B. Interpreting Complex Models V. Conclusion VI. FAQs

Article:

Introduction

Machine learning has become an increasingly important tool in a variety of industries, from finance to medicine. However, as these models become more complex and are applied to more critical tasks, the need for explainability has become paramount. In this article, we will explore the importance of explainability in machine learning and provide two case studies to illustrate different approaches to achieving it.

The Importance of Explainability in Machine Learning

As machine learning models become more complex and are applied to more critical tasks, the need for explainability has become increasingly important. In many cases, the decisions made by these models can have a significant impact on people's lives, and it is essential that these decisions can be understood and justified.

There are several reasons why explainability is important in machine learning. First, it can help to prevent unfairness and discrimination. If a model is making decisions Based on factors that are correlated with protected attributes such as race or gender, it can lead to unfair outcomes. By understanding how the model is making its decisions, we can identify and address these issues.

Second, explainability can help to build trust in machine learning models. If people can understand how a model is making its decisions, they are more likely to trust it. This is particularly important in industries such as finance and medicine, where the decisions made by these models can have a significant impact on people's lives.

Case Study 1: Fair Lending

In our first case study, we will explore the use of explainability in fair lending. Lenders are required to explain their rejections under fair lending laws, and it is essential that these explanations are understandable and justifiable.

We will explore three different approaches to achieving explainability in fair lending: logistic regression, boosted trees, and kernel SHAP.

Logistic Regression

Logistic regression is a simple and interpretable model that assigns importance to each input based on its weight. By looking at the weights assigned to each input, we can understand how the model is making its decisions.

For example, if We Are trying to predict the likelihood of default on a loan, we might find that the number of open accounts and the length of employment are negatively correlated with default, while the debt-to-income ratio is positively correlated. By understanding these relationships, we can identify ways to improve our creditworthiness and increase our chances of being approved for a loan.

Boosted Trees

Boosted trees are a more complex model that can achieve higher performance but are less interpretable. However, we can still use techniques such as feature importance to understand how the model is making its decisions.

For example, we might find that the FICO score is the most important feature in predicting default, followed by the loan amount and the number of accounts open in the past 24 months. By understanding these relationships, we can identify ways to improve our creditworthiness and increase our chances of being approved for a loan.

Kernel SHAP

Kernel SHAP is a technique for explaining the predictions of complex models such as boosted trees. It works by attributing the prediction to each input feature, allowing us to understand how the model is making its decisions.

For example, we might find that the loan amount has the highest blame for a particular loan application being rejected, followed by the FICO score and the number of accounts open in the past 24 months. By understanding these relationships, we can identify ways to improve our creditworthiness and increase our chances of being approved for a loan.

Case Study 2: Medicine

In our second case study, we will explore the use of explainability in medicine. Machine learning models are increasingly being used to predict outcomes such as death from pneumonia or readmission to the hospital. However, these models can be prone to correlation is not causation problems, where they learn something that is correlated with the outcome but not causally related.

We will explore the use of generalized additive models (GAMs) to achieve explainability in medicine.

Generalized Additive Models

GAMs are a Type of model that can add complexity to models without damaging their interpretability. They work by learning a set of functions that transform a single input, which are then passed through a link function to produce the output.

For example, if we are trying to predict the risk of death from pneumonia, we might find that age is a significant factor, but that the relationship is nonlinear. By using a GAM, we can learn a nonlinear function that captures this relationship, allowing us to better understand how age affects the risk of death.

Interpreting Complex Models

In addition to using GAMs, there are other techniques that can be used to interpret complex models. For example, we can use feature importance to identify the most important features in the model, or we can use permutation-based methods to identify the impact of each feature on the model's predictions.

However, it is important to remember that these techniques are not always sufficient for achieving explainability in complex models. In some cases, it may be necessary to use simpler models or to rely on human intuition to understand how the model is making its decisions.

Conclusion

Explainability is an essential aspect of machine learning, particularly in critical industries such as finance and medicine. By understanding how models are making their decisions, we can identify and address issues such as unfairness and discrimination, build trust in these models, and improve their performance.

In this article, we have explored two case studies to illustrate different approaches to achieving explainability in machine learning. In fair lending, we explored the use of logistic regression, boosted trees, and kernel SHAP. In medicine, we explored the use of generalized additive models.

While there is no one-size-fits-all approach to achieving explainability in machine learning, there are many techniques and tools available to help us better understand these models and their decisions.

FAQs

Q: Why is explainability important in machine learning? A: Explainability is important in machine learning because it can help to prevent unfairness and discrimination, build trust in these models, and improve their performance.

Q: What are some techniques for achieving explainability in machine learning? A: Some techniques for achieving explainability in machine learning include logistic regression, boosted trees, kernel SHAP, and generalized additive models.

Q: How can explainability help to prevent unfairness and discrimination in machine learning? A: By understanding how models are making their decisions, we can identify and address issues such as unfairness and discrimination.

Q: What are some challenges to achieving explainability in machine learning? A: Some challenges to achieving explainability in machine learning include the complexity of models, the lack of interpretability of some inputs, and the need to balance performance with interpretability.