Importance of Interpretability in Machine Learning Models

Explore interpretability in machine learning models and important facets around it to learn how it impacts feature outputs and more.

Machine learning models are trained on large datasets to generate outputs based on learned features. This process can introduce bias and issues within the ML model. Interpretability in machine learning helps identify and fix these problems, ensuring transparency and fairness. It improves model reliability, making AI systems more accountable and trustworthy.

Keeping this thought in mind, our core endeavor with this editorial was to provide a detailed answer to the query, “What is interpretability in machine learning?.” So, let's start dissecting the topic, layer by layer, to control feature outputs and make necessary updates to our ML model.

What is Interpretability in Machine Learning?

Interpretability in machine learning is the ability to determine the effect of a cause within an AI system. Interpretability stems from interpretable machine learning which aims to both develop and analyze an AI model. 

Importance of Interpretability in Machine ML

To understand the importance of interpretability in machine learning, we need to learn about the four pillars of it. These are:

Building Trust

As much as experts advocate artificial intelligence, the trust around the tech is mostly volatile in nature. The reason is simple: AI models have had allegations of training their models with user data. Also, the image of AI tech has been largely negative because of the media, where it has primarily been made a villain.  So, interpretability in ML plays a huge role in establishing this trust. People can make use of interpretability to take a peak into the mechanisms behind an ML model. The analogy would be to check the brain of the ML model such that it boosts confidence and better decision-making in terms of adoption.

Debugging and Bias Detection 

Every machine learning tool and model showcases possible flaws and limitations. Interpretability reinforces the decision-making process for engineers and developers to troubleshoot the ML models. It helps understand why the ML models act a certain way and the places where they can’t comprehend, helping you reveal existing issues and remove any bias within the system.

Regulatory Compliance 

To address consumer concerns, it becomes important for the AI vendor to showcase how their data is being used. Even if the customers stay away from it, there are multiple compliances that compel AI development companies to involve interpretability in their ML Models. Why? Because it gives a segue to consumers who want an explanation for the decisions made by the ML model. 

Some of the compliances to keep in mind are:

• GDPR (General Data Protection Regulation)
• California Consumer Privacy Act (CCPA)
• Equal Credit Opportunity Act (ECOA)
• AI Act (European Union)
• Financial Industry Regulations

Improved Model Development

Knowing how machine learning works can help in making new changes to the model and growing a company. 

Interpretability means understanding the factors that affect a model's predictions. Using interpretability in machine learning, a company can change its strategies and make updates to its ML model. Insights from it can help a company figure out how customers act to sell more. 

Types of Interpretability in Machine Learning

When it comes to assessing interpretability, there are several types that help establish trust, deliver debugging capabilities, and promote fairness of the models. So, here are the types.

1. Global Interpretability: Used to understand how a model works for all inputs and their behavior. 
2. Local Interpretability: Helps understand how the model works for a single input.
3. Post-hoc Interpretability: It helps in understanding how a model works after training.
4. Intrinsic Interpretability: Interpretability that is self-explanatory.
5. Feature-based Interpretability: Helps in finding the most important features.
6. Model-specific vs Model-agnostic Interpretability: Model-specific works for certain models, while model-agnostic works on all models.
7. Process Interpretability: It enables learning how the model learns during training and its improvement over time.
8. Casual Interpretability: Explains predictions based on cause and effect to see how one feature changes the prediction.
9. Ethical Interpretability: Makes sure that the model is fair and unbiased making trustworthy decisions.

Challenges in Achieving Interpretability in ML

The ecosystem around interpretability in machine learning is still evolving. So, there are several challenges in achieving interpretability in ML, such as:

• Complexity: Advanced models like deep neural networks based on machine learning are highly complex, making it difficult to trace input-output relationships 
• Interpretability and Accuracy Trade-Off: Simple models are accurate but lack in performing complex tasks. Contrarily, complex models are accurate but lack interpretability.
• Lack of Standardization: Varies across industries with no metric to define interpretability exists universally.
• Scalability: Interpretability methods can’t be scaled to large datasets or models, leaving computational feasibility for real-time applications.
• Reproducibility Issues: Reproduction of results is reliant on hardware and complexity of the model. It varies if the hardware or the complexity changes.
• Transparency: Technical details around the model may not always reveal the specific behavior of a model.
• Black Box Nature: Non-linear and parameter-rich models don’t always have a straightforward explanation.
• Bias in Interpretability Methods: Interpretability techniques can introduce bias or might focus on irrelevant features, misleading stakeholders and eroding trust.
• Security Concerns: Models that are highly interpretable might expose vulnerabilities to attacks, increasing the risk of model weaknesses.
• Cultural and Ethical Differences: The degree of interpretability required differs across cultures and regions. 

Machine Learning Interpretability Methods - Technique to Assess ML Models

Interpretability is a quality that lets an ML model exhibit its internal nuances. To reach those nuances, you need to learn about the different machine learning interpretability methods. 

These methods are primarily of two types, i.e., Model Specific Techniques and Model Agnostic Techniques. Here’s the explanation for both.

Model Specific Techniques

These are techniques that are used to figure out the internal workings of an ML model for a particular model. Let’s explore different model-specific techniques used for interpretability in ML models.

Linear Regression

Linear regression is a well-known method used by Mathematicians, AI experts, researchers, etc. It shows how features and their output are related to mathematical equations. The equation is:

y = b₀ + b₁x

Here:

• y: Represent the predicted value (dependent variable) that the model aims to predict.
• x: It is the independent variable (predictor) used to depict different features of the model.
• b₀: This is the intercept (the value of y when x is 0) used to determine the prediction value when independent variables are 0.
• b₁: It is the slope (the change in y for a unit change in x) that determines the strength of the relationship between input and output.

For ML models, multiple linear regression is used. Its formula is:

y = b₀ + b₁x₁ + b₂x₂ + ... + bₙxₙ

Here, except for the values provided above, the remaining ones are:

• x₁, x₂, ..., xₙ: These are the independent variables (predictors).
• b₁, b₂, ..., bₙ: They are the coefficients for each independent variable.

This data can be used to plot a graph to visualize the relationship between features and the output. In simple linear regression, we expect to see a linear trend. Deviations from this linear trend can indicate a non-linear relationship between the variables, suggesting that a more complex model might be necessary to accurately capture the underlying patterns in the data. It's important to note that some deviations may also be due to noise or random fluctuations.

Decision Trees

Decision Tree is another popular choice to analyze interpretability in machine learning. Decision trees are used where the relationship between the feature and output is not linear. Additionally, decision trees are inherently interpretable, follow a series of “if-then-else” rules, and the layout is very straightforward. It is important to know that features that appear higher in a tree or are used frequently for splitting data are considered more important.

Rule-Based Systems

Rule-based systems operate on a set of predefined rules, i.e., “If-Then” statements. Each of these rules is defined by human experts, and a direct mapping is conducted between input and output data. So, for a model to make a particular decision, it needs to pass through a particular chain of rules that are triggered by the input data.

The rules themselves are provided in a human-readable format so it becomes easy for domain experts to understand and validate. Also, better communication and collaboration are facilitated through this between developers and domain experts.

Examples of rules are:

• IF the transaction amount>$10,000 AND the location of the transaction is outside the country of card origin THEN flag it as fraudulent.
• IF credit score>750 AND debt-to-income ratio <0.3 THEN approve the loan.
• IF email contains more than 5 exclamation points (!) THEN flag it as spam.

Model Agnostic Techniques

Model Agnostic Techniques treat an ML model like a black box and determine its capabilities in correlation with input and output. Some of the model-agnostic techniques used are:

Feature Importance

Feature importance is a way to assess the contribution of features within a model. Inputs that are important are scored higher while the remaining have a lower score. This helps prioritize features that are important to the working of the model and even reduces work in assessment.

For a better understanding, consider the analogy of grocery shopping for a dish made of chicken. So, shopping for chicken will have a higher value, while other ingredients might take a lesser precedence.

Some machine learning interpretable methods used in feature importance are:

• Gini Importance: Used in tree-based models like decision trees and random forests, it helps measure a feature's impact to reduce impurities, also known as Gini impurity, at each node in the tree. In Gini importance, a higher impurity reduction means higher importance.
• Permutation Importance: It is a model-agnostic method that shuffles the value of a single feature to assess the decrease in the performance of the model. A large performance drop means higher feature importance.
• Linear Model Coefficients: Applied to linear models like Linear Regression and Logistic Regression where the magnitude of coefficients reflects the feature importance. Interpretation with linear model coefficients becomes complex with high feature correlations.
• SHAP Values (SHapley Additive exPlanations): Advanced and game-theoretic approach that is more accurate and offers interpretable measures for feature importance. However, it is computationally expensive, at least for complex models.

Local Interpretable Model-Agnostic Explanations (LIME)

LIME is a model-agnostic approach that works in analyzing the behavior of any type of machine learning model even the ones that are considered “Black Boxes.” It uses approximation to assess the behavior around the original model for a specific data point using a simpler model like a linear model or decision tree.

Below are the key steps taken for the process:

• Perturbation: Generate perturbed instances of the data point in interest and slightly modify the feature values.
• Prediction: Use the original black box model to predict the outcome for the perturbed instances.
• Weighting: Assign weight to the perturbed instances to assess their proximity with the original data point. Instances that are closer to the original point have higher weights.
• Local Model Training: Start training a simple interpretable model based on the weights assigned to the perturbed instances and the predictions from the black box.
• Explanation: The coefficients generated from the interpretable model will provide insights into the features that influence the prediction of a specific data point.

Why is this model effective? It works with any type of machine learning model (as mentioned above), provides insights on specific data points, and is easy to understand.

Shapley Values

Shapley values are a borrowed concept from game theory. It emerged as a powerful tool that can help with the interpretability of machine learning models. These values can be applied to any machine learning model with varying complexity or type. 

In this method, you assign value to reach feature depending upon its contribution to the model’s output. Further on, the foundation of Game Theory is used with Shapley values. They represent the idea of a “fair” distribution of the ”payout” (the model’s prediction) amongst the “players” (the features). 

Now let’s assess the steps taken:

1. Coalition Formation: Imagine all the features as the players in the game, and consider all possible combinations of features.
2. Marginal Contribution: For each feature, start calculating their marginal contribution based on the models when combined with a specific coalition.
3. Average Contribution: Take the average of marginal contributions for all possible combinations for each feature.

Partial Dependence Plots (PDPs)

Partial Dependence Plots are a tool to understand the relationship between the feature and their predicted outputs from a machine learning model. It works by creating a visual representation of the average predicted outcome against the values generated for the feature while keeping other features constant.

PDPs can be used to unravel non-linear relationships between the feature and its target variable. Here, a positive slope means a positive relationship and vice versa.

Visualizations Techniques

Visualization is a common technique applied to the aforementioned machine learning interpretable methods. It is crucial for understanding and communicating the insights gathered from an interpretable machine learning model. To materialize the earlier thought, these are some of the plots used alongside interpretable methods.

1. Feature Importance Plots
2. Partial Dependence Plots
3. Individual Conditional Expectation (ICE) Plots
4. SHAP (SHapley Additive exPlanations) Visualizations
5. LIME
6. Decision Tree Visualizations
7. Activation Maps

Applications of Interpretable Machine Learning

The importance of interpretability in ML is at par, considering its applications in different industries. Just to give you a perspective, here are some common applications:

1. Healthcare

Interpretability in machine learning for healthcare is a critical research area. It helps physicians gain insights from the predictions of the ML model and helps understand various diseases and medical conditions. Some examples are:

1. Heart Failure Risk Prediction: Interpretability analysis helps in predicting risk scores for patients prone to heart failures.
2. Glaucoma Diagnosis: Multiple ML algorithms like XGBoost are used for diagnosing glaucoma by processing retinal images. Interpretability methods like SHAP, radar diagram, etc., can be used to interpret.
3. Breast Cancer Prediction: LIME can be applied to check the robustness and fidelity of the model that predicts breast cancer.

Other than these applications, interpretability can be used for lung cancer mortality prediction, SARS-CoV-2 Infection Severity, and many others.

2. Finance

Interpretable machine learning (IML) can be used at par in finance applications. Some key areas are:

1. Credit Scoring and Risk Assessment: IML can be used to find biases in credit scoring models, and it explains why a loan was approved or denied.
2. Fraud Detection: Interpretability can explain fraud alerts and help investigators with suspicious cases. It can also be used to determine false alarms for real transactions.
3. Investment Portfolio Management: IML can debunk how a model provides investment advice and help identify the risk + return of investments.

3. Criminal Justice

Some of the spaces where machine learning interpretability can help in criminal justice are:

1. Risk Assessment: Interpretable models can show the reason behind someone’s crime and even share why someone is given bail.
2. Sentencing Guidelines: IML can help ensure that the reason behind sentencing is fair and based on the right factors.
3. Policing and Investigations: These models can help with finding places where crimes are likely to happen.

4. Environmental Science

Traditional machine learning models were good in terms of predicting outcomes. However, they were not transparent, and with the “black box” nature of current models, interpretability is the way to go. In environmental science, it is important to use interpretable machine learning as it can help uncover the black box side, too. Considering this reason, some of the key applications of IML are:

1. Air Quality and Pollution: IML can be used to create a correlation between weather factors and regional characteristics to determine air quality. It can help uncover dynamic values that contribute to particulate matter concentrations in air.
2. Climate Patterns: Models used for predicting precipitation patterns have become more interpretable, showing different factors that interact to affect weather and climate.
3. Evapotranspiration in Crops: Researchers can identify key water and climate factors to study evapotranspiration helping in crop management and irrigation strategies.

Future Prospects - Possible Developments

Machine learning interpretable methods are consistently evolving. This fact, combined with current issues related to interpretability, will give way to plenty of future prospects, discussed below:

1. Studying Interpretability: Possible discovery of solutions for assessing interpretability in heterogeneous ML models. 
2. Temporal Data Interpretability: Models to analyze temporal data will see progress for static and dynamic information.
3. Adoption of Model-Agnostic Tools: Modularity and scalability offered by Model-Agnostic Tools will see higher adoption rates in diverse industries. 
4. Fully Automated ML Pipelines: Automated training and interpretability tools will help democratize machine learning, enabling non-experts to leverage the power of ML.
5. Black-Box Analysis Innovations: Statistical methods that are more transparent and interpretable will evolve for black-box models.
6. Industry Standardization: Standardization of processes and workflow will emerge as automation increases.
7. Interpretability as a Mandate: The future might see the interpretability of machine learning models as a core constraint, making models more understandable from scratch.
8. More Trust: With interpretability in machine learning taking the stage, the confidence of the users will be boosted because of more transparency and intuitive explanations.
9. Advances: Research in interpretability might unlock new approaches to building and understanding intelligence.

Interpretability vs Explainability in Machine Learning

Both interpretability and explainability in machine learning let you take a peek inside working ML models. But how do they differ, well, here’s a table:

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End Note!

Interpretability in machine learning is an important value if you work in the domain of AI. It helps you figure out the necessary changes you need to make within a model and puts the output control within your hands. By answering the question “What is interpretability in machine learning?” we aimed to provide you insights into a topic that is important and will gain more precedence in the future. Saying this, we hope we may have succeeded in our task. And, if you want more similar resources, check out our blog section to get insights on many other topics.