A Comprehensive Guide to Applied Machine Learning Explainability Techniques

Machine learning models have become increasingly powerful, but their lack of transparency can make it difficult to understand why they're making certain predictions. This is where applied machine learning explainability techniques come in.

These techniques provide insight into how a model is making its predictions, allowing us to identify potential biases and areas for improvement. By understanding how a model is working, we can build more trustworthy and reliable models.

One key aspect of explainability is feature importance, which helps us understand which input features are driving a model's predictions. For example, a model might assign high importance to a user's location when predicting their likelihood of buying a product.

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Applied machine learning explainability is a crucial aspect of building trust and reliability in AI systems. It involves providing clear and understandable explanations of AI model decisions, which is essential for applications where decisions have significant impacts, such as healthcare and finance.

By making the decision-making process transparent, XAI fosters trust among users and stakeholders. This is particularly important in sectors where understanding the rationale behind AI predictions is essential for user acceptance. In autonomous driving, for example, XAI helps verify that AI models function as intended, allowing engineers and developers to validate and improve the reliability of these systems.

There are various techniques for achieving machine learning explainability, including symbolic-based models, interpretable attention mechanisms, and prototype-based models. These models effectively merge predictive capabilities with transparency, making them highly interpretable and user-friendly. They offer analytical parallels to the model's predictions, allowing developers to understand how different elements influence the model's decisions.

Here are some notable models that demonstrate significant progress in making deep learning more interpretable:

Learning is a fundamental concept in machine learning, and it's the process by which models develop and improve over time. Machine learning models learn relationships between input and output data.

These relationships are identified by the model itself, which can be a challenge for human developers to understand. The model can then make decisions based on patterns and relationships that may be unknown to humans.

Machine learning models learn from experience with data, which is why they can be difficult to understand once they're deployed. This is especially true for complex models like deep neural networks.

At its core, machine learning models aim to classify new data or predict trends, but the underlying functionality is developed by the system itself. This is why model explainability is crucial for human specialists to understand the algorithm behind the decision.

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Applied Machine Learning Explainability is a crucial aspect of AI development, ensuring that the decisions made by machine learning models are transparent and understandable to humans.

It's a response to the black box nature of traditional machine learning models, which can be difficult to interpret and understand.

Machine learning models are often complex and opaque, making it hard to discern how they arrive at their predictions or decisions.

This lack of transparency can lead to mistrust and skepticism, particularly in high-stakes applications like healthcare or finance.

Explainability techniques aim to provide insights into the decision-making process of machine learning models, enabling developers to identify biases and errors.

By applying explainability techniques, developers can improve the fairness, accuracy, and reliability of their models.

Explainability techniques can be broadly classified into three main areas: local model explainability, cohort model explainability, and global model explainability. These categories help us understand how to approach model interpretability in different contexts.

Local model explainability focuses on individual predictions, providing insights into why a model made a specific decision. Techniques like LIME and SHAP are used to generate feature importance scores, which help identify the most relevant features contributing to a prediction. This approach is particularly useful in applications like patient diagnosis in healthcare, where understanding the decision-making process is crucial.

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Cohort model explainability examines the behavior of a model across a group of instances, offering a more comprehensive view of the model's performance. This technique is useful for identifying trends and patterns in the data, such as understanding why a model is misclassifying certain types of images. By analyzing the behavior of a model across a cohort of instances, we can gain a deeper understanding of the model's strengths and weaknesses.

Global model explainability provides an overarching view of the model's behavior, elucidating general patterns and rules. This approach is essential for contexts requiring comprehensive understanding and transparency, such as policy-making. By examining the model's behavior globally, we can identify biases and areas for improvement, ultimately leading to more reliable and trustworthy models.

Explainable AI offers various explanation types to address different aspects of a model's decision-making process. There are six main types of explanations: Feature Importance Explanations, Example-Based Explanations, Counterfactual Explanations, Local Explanations, Global Explanations, and Causal Explanations.

Feature Importance Explanations identify and rank features by their impact on the model's predictions. This is crucial in areas like finance and healthcare for understanding feature relevance. In healthcare, for instance, feature importance explanations can help identify the most critical factors in patient diagnosis.

Example-Based Explanations use specific instances to demonstrate the model's behavior. They are effective in fields where concrete examples are more illustrative, such as image or speech recognition. In image recognition, for example, example-based explanations can help identify the key features that led to a particular classification.

Counterfactual Explanations explain what changes in inputs could lead to different outcomes. This offers actionable insights for scenarios requiring understanding of outcome alterations. In finance, for instance, counterfactual explanations can help identify the key factors that led to a particular investment decision.

Local Explanations focus on individual predictions to explain why the model made a certain decision. They use tools like LIME and SHAP, which are key in applications needing in-depth insights into singular decisions, like patient diagnosis in healthcare.

Global Explanations provide an overarching view of the model's behavior, elucidating general patterns and rules. This is essential for contexts requiring comprehensive understanding and transparency, such as policy-making. In policy-making, global explanations can help identify the key factors that influence a particular decision.

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Causal Explanations delve into cause-and-effect relationships within the decision process. This is vital for fields where understanding these dynamics is crucial, like scientific research and economics. In scientific research, for instance, causal explanations can help identify the key factors that led to a particular outcome.

Here are the six types of explanations in a concise list:

CEM is designed to be applied locally, which means it provides explanations for specific instances rather than the entire model.

CEM generates instance-based local black box explanations, highlighting what should be minimally and sufficiently present to justify a classification, known as Pertinent Positives (PP).

CEM also identifies what should be minimally and necessarily absent, called Pertinent Negatives (PN), to form a more complete explanation.

CEM works by defining why a certain event occurred in contrast to another event, helping developers deduce "why did X occur instead of Y?".

CEM is used to provide explanations for classification models by identifying both preferable and unwanted features in a model.

CEM provides a more complete and well-rounded explanation by considering both Pertinent Positives and Pertinent Negatives.

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Game Theoretic methods offer a unique perspective on AI interpretability by treating input features as players in a cooperative game. This approach provides insights into the contributions and sensitivities of features within AI models.

Shapley Values, derived from cooperative game theory, distribute a model's output among its features based on their contribution, offering a fair understanding of each feature's impact on the model's decision. However, they can be computationally heavy for models with many features.

The Least Core concept, also from cooperative game theory, examines the stability of the model by identifying the minimal feature value change necessary to significantly alter the model's output. This highlights sensitive features in the model.

Both Shapley Values and the Least Core may not fully account for feature interactions, potentially simplifying interpretability in models with interconnected features.

Here are some key aspects of Game Theoretic methods:

These game theoretic methods provide a framework for understanding and interpreting the contributions and sensitivities of features within AI models, each with its own set of challenges and computational considerations.

Post-hoc interpretability techniques are used to understand complex machine learning models that are inherently difficult to interpret. These methods are applied after the model has been trained and include feature importance scores, partial dependence plots, and LIME (Local Interpretable Model-agnostic Explanations).

LIME is a popular post-hoc explanation algorithm that takes decisions and builds an interpretable model to represent the decision. It then uses this model to provide explanations, making it easier to understand the model's behavior.

Post-hoc methods are often used to provide a deeper understanding of model behavior, often through visualizations. However, they rely on proxies, which can make their claims to interpretability questionable.

Here are some key characteristics of post-hoc methods:

Probabilistic models are a great choice for tasks that require high interpretability. They're known for their transparent approach and inherent interpretability, which makes them a popular choice in machine learning.

One type of probabilistic model is graphical models, which utilize graph-based representations to depict conditional dependencies between variables. This visual nature enhances comprehensibility and effectively handles uncertainty and incomplete data.

Graphical models like Bayesian networks and Markov models are great for understanding complex relationships and data structures. However, they can become complex with more variables and require solid domain knowledge for correct setup.

Time series models, on the other hand, are particularly useful for tasks that involve forecasting and predicting future values. Models like SARIMA and Prophet are examples of time series models that can provide deep insights into data structures and decision-making processes.

Here are some key characteristics of probabilistic models:

Overall, probabilistic models offer a transparent and interpretable approach to machine learning, making them a valuable tool for a wide range of applications.

Post-Hoc Blackbox Methods are used to interpret models that are inherently complex and opaque, like deep neural networks or ensemble methods. These techniques are applied after the model has been trained and include feature importance scores, partial dependence plots, and LIME (Local Interpretable Model-agnostic Explanations).

Post-hoc methods are model-agnostic, meaning they can be applied to any machine learning model. They provide insightful analysis, often through visualizations, but their reliance on proxies makes many of their claims to interpretability questionable.

One commonly used post-hoc explanation algorithm is LIME, or Local Interpretable Model-agnostic Explanation. LIME takes decisions and builds an interpretable model that represents the decision, then uses that model to provide explanations.

LIME works by perturbing any individual data point and generating synthetic data which gets evaluated by the black-box system and ultimately used as a training set for the glass-box model. LIME has been designed to be applied locally.

Here are some key characteristics of post-hoc blackbox methods:

Gradient-based methods are a type of post-hoc interpretability technique that attribute importance to input features by analyzing the gradients of the model output with respect to the input.

Integrated gradients method calculates the gradients of the prediction output with respect to the features of the input, along an integral path. This involves calculating the gradients at different intervals of a scaling parameter, and then integrating them to attribute importance to each input feature.

The integrated gradients method is designed to be applied locally, making it a useful tool for understanding feature importances and identifying data skew. It's also useful for debugging model performance, as it can help pinpoint which features are most responsible for a particular prediction.

Here are some key benefits of using the integrated gradients method:

The integrated gradients method is a powerful tool for understanding how machine learning models make predictions, and can be used to improve model performance and reduce bias.

Counterfactual Instances are a type of explanation that 'interrogate' a model to show how individual feature values would have to change in order to flip the overall prediction. This method takes the form of "If had not occurred, would not have occurred".

A Counterfactual Instance is designed to be applied locally, which means it focuses on a specific instance of interest and the label predicted by the model. This approach can be useful in understanding how small changes in input data can lead to significant changes in output predictions.

By using Counterfactual Instances, you can identify what changes in inputs could lead to different outcomes, offering actionable insights for scenarios requiring understanding of outcome alterations. This is particularly useful in fields where understanding these dynamics is crucial, such as scientific research and economics.

For instance, if a model misclassifies a bird as a plane, Counterfactual Instances can help identify what changes in the input data would have led to a correct classification.

Bayesian Rule Lists are a type of post-hoc explanation technique that helps explain a model's predictions by combining pre-mined frequent patterns into a decision list generated by a Bayesian statistics algorithm. This list is composed of "if-then" rules, where the antecedents are mined from the data set and the set of rules and their order are learned.

Scalable Bayesian Rule Lists can be used both globally and locally to provide explanations for a model's predictions. They have a logical structure that's a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree.

SBRLs are particularly useful for tasks requiring high interpretability, such as understanding complex relationships and data structures. They effectively handle uncertainty and incomplete data.

Here are some key characteristics of Scalable Bayesian Rule Lists:

In addition to Scalable Bayesian Rule Lists, another notable technique is Graphical Models, which utilize graph-based representations to depict conditional dependencies between variables. This visual nature enhances comprehensibility and is particularly useful for understanding complex relationships and data structures.

Feature attribution and interpretability are crucial aspects of applied machine learning explainability techniques. Feature attribution methods, such as Shapley values, assign credit to each feature for a particular outcome, providing insights into the decision-making process.

These methods can be applied to various machine learning models, including decision trees, linear models, and rule-based systems. In fact, Shapley values are used in Vertex Explainable AI to assign proportional credit to each feature for the outcome of a particular prediction.

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Some popular feature attribution methods include sampled Shapley, integrated gradients, and XRAI. Sampled Shapley assigns credit to each feature by considering different permutations of the features, while integrated gradients efficiently compute feature attributions with the same axiomatic properties as the Shapley value. XRAI, on the other hand, assesses overlapping regions of the image to create a saliency map, highlighting relevant regions of the image.

Here are some key benefits and characteristics of each method:

These methods can be used to provide insights into the decision-making process of machine learning models, helping to build trust and understanding in the models' predictions.

Feature attribution is a crucial aspect of feature attribution and interpretability. It helps us understand how each feature in our model contributes to the predictions.

Feature attribution methods include sampled Shapley, integrated gradients, and XRAI, which are available in Vertex Explainable AI. These methods provide a sampling approximation of exact Shapley values, gradients-based computations, and saliency maps, respectively.

Sampled Shapley is particularly useful for non-differentiable models, such as ensembles of trees and neural networks. Integrated gradients, on the other hand, is recommended for differentiable models with large feature spaces, especially for low-contrast images.

XRAI is based on the integrated gradients method and assesses overlapping regions of the image to create a saliency map. It's recommended for models that accept image inputs, especially for natural images.

Feature attributions indicate how much each feature contributed to the predictions for each given instance. When you request explanations, you get the predicted values along with feature attribution information. This is supported for all types of models, frameworks, BigQuery ML models, and modalities.

SHapley Additive exPlanations (SHAP) is another common algorithm that explains a given prediction by mathematically computing how each feature contributed to the prediction. It functions largely as a visualization tool, making the output of a machine learning model more understandable.

Accumulated Local Effects (ALE) is a method for computing feature effects. The algorithm provides model-agnostic global explanations for classification and regression models on tabular data. ALE addresses some key shortcomings of Partial Dependence Plots (PDP).

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Here are some key feature attribution methods and their characteristics:

To create meaningful explanations, it's essential to consider the needs of your target audience. The effectiveness of an explanation is gauged by several key qualities, including accuracy, fidelity, comprehensibility, and certainty.

Accuracy is crucial for predictions, but lower accuracy may be acceptable if it aligns with the model's accuracy. Fidelity is vital for truly understanding and trusting the model, particularly in critical applications.

Comprehensibility is concerned with the ease with which the target audience can grasp the explanation. Influenced by the complexity of the explanation and the audience's background knowledge, comprehensibility is crucial for user acceptance, trust, and collaborative interactions.

A meaningful explanation should be provided in a way that the intended users can understand. If there is a range of users with diverse knowledge and skill sets, the system should provide a range of explanations to meet the needs of those users.

Here are some examples of how to provide meaningful explanations:

By providing meaningful explanations, you can improve trust, user acceptance, and collaborative interactions. This is particularly important in environments that are regulated or audited, such as banking and healthcare.

Local and global interpretability are two approaches to understanding how machine learning models make decisions. Local model explainability focuses on specific decisions made by the model, helping to answer questions about why a particular decision was made.

This approach is useful in regulated organizations, where it's essential to justify business decisions made by the model. Local model explainability can also help identify the features that most contributed to a specific error or outlier, allowing for ongoing improvement and optimization.

Global model explainability, on the other hand, provides a holistic view of the model's behavior, highlighting the features that have the most impact on all of the model's outcomes or decisions. This approach is commonly used to answer top-line questions about how the model performs overall after deployment.

Here are the key differences between local and global interpretability:

Both local and global interpretability are essential for understanding how machine learning models make decisions, and can be used to improve model performance and transparency.

GIRP, or Global Interpretation via Recursive Partitioning, is a compact binary tree that interprets ML models globally by representing the most important decision rules implicitly contained in the model using a contribution matrix of input variables.

This approach can only be applied globally, which sets it apart from other model interpretation methods. It's a powerful tool for understanding how a model makes predictions, but it's not suitable for local or individual model decisions.

To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces. This process helps to identify the most important features driving the model's predictions.

GIRP is a valuable addition to any model's toolkit, providing a clear and concise way to understand the model's decision-making process. By leveraging this approach, organizations can gain a deeper understanding of their models and make more informed decisions.

Local model explainability is an approach used to understand individual model decisions. It's particularly useful for models deployed in regulated organizations, as it helps them justify business decisions made by the model.

Local model explainability tools, like LIME, can be used to understand which specific features impacted a specific decision. For example, if a mortgage application was rejected by a model, LIME can help identify which features, such as credit score or income, contributed to the rejection.

Local model explainability is often used in very specific circumstances, such as when a client or stakeholder has a query about a model's decision. It's also useful for detecting model drift, such as machine learning concept drift, where models become inaccurate over time.

Here are the three main areas of machine learning explainability tools, including local model explainability:

Local model explainability is useful for understanding individual predictions, while global model explainability provides an overarching view of the model's behavior. Cohort model explainability, on the other hand, helps understand the behavior of a group of instances.

Local model explainability is a key aspect of machine learning interpretability, and it's essential for building trust in AI models. By understanding how individual models make decisions, we can improve their performance and fairness.

Global models are a type of approach used in Explainable AI. They focus on the features that have the most impact on all of the model's outcomes or decisions, providing a holistic view of how the model functions and makes decisions.

Global model explainability is often used during the model deployment phase to answer top-line questions about how the model performs overall. It's a common approach used by stakeholders with no prior data science experience because it provides a global or top-level understanding of the model.

This approach can be used to understand the model in more detail, highlighting the features that have the biggest impact on the average model decision. Data scientists can use global model explainability to identify and resolve over-reliance on specific features that may cause bias.

Here are the different types of global explanations:

These types of explanations are essential for contexts requiring comprehensive understanding and transparency, such as policy-making.