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What is the difference between local and global interpretability in ANNs?

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Artificial neural networks (ANNs) are powerful and complex models that can learn from data and perform various tasks, such as classification, regression, and generation. However, ANNs are often considered as black boxes, meaning that their internal workings are not easily understandable or transparent. This poses a challenge for users and stakeholders who want to trust, validate, and improve the models. Interpretability is the ability to explain how and why a model makes its decisions, and it can be achieved at different levels: local and global. In this article, you will learn what is the difference between local and global interpretability in ANNs, and why they are both important.

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1 Local interpretability

Local interpretability refers to explaining the model's behavior for a specific input, output, or instance. For example, you might want to know why an ANN classified an image as a cat or a dog, or what features influenced its prediction for a certain customer. This type of interpretability can help you understand the model's strengths and weaknesses, identify errors and biases, and provide feedback for improvement. Saliency maps, feature attribution, and counterfactuals are some of the methods used to achieve local interpretability. Saliency maps are visualizations that highlight the regions or pixels of an input image that contribute the most to the model's output, allowing you to see what the model is focusing on and ignoring. Feature attribution assigns numerical values that measure the importance or relevance of each input feature for the model's output, helping you quantify the model's dependence on certain features and their interactions. Counterfactuals are hypothetical scenarios that show how the model's output would change if some input features were modified, allowing you to explore the model's sensitivity and robustness to different inputs.

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Dr. Priyanka Singh Ph.D.

?? AI Author ?? Transforming Generative AI ?? Responsible AI - Lead MLOps @ Universal AI ?? Championing AI Ethics & Governance ?? Top Voice | Empowering Future AI Solutions | Packt Technical Reviewer
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Local interpretability aims at explaining machine learning models' behavior concerning specific inputs or instances, offering insights into models' precise decisions and identifying potential biases or errors for individual cases. In contrast, global interpretability seeks to understand the model's overall behavior and decisions across all data. It provides a broad overview of the most influential features and their impact on the model's predictions. Both are crucial for a comprehensive understanding of artificial neural networks (ANNs), and the choice between them depends on the desired level of insight and the application context.

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Joe H ☆

Data Science | AI, ML, Semantic Knowledge Graphs, Computer Vision
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Global interpretability looks at the overall behavior of a neural network model across the entire dataset. It provides insights into the model's general decision-making patterns and the features that are consistently influential across different data points. Techniques for global interpretability include methods like feature importance ranking, model visualization, and aggregation of local interpretability results. These methods help identify the most important features for the model as a whole.

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Joe H ☆

Data Science | AI, ML, Semantic Knowledge Graphs, Computer Vision
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Local interpretability focuses on understanding how a neural network model arrives at a specific prediction for a single data point or a small subset of data points. It aims to explain the model's decision-making process in a granular and instance-specific way. Techniques for local interpretability include methods like feature attribution or feature importance scores. These techniques assign a relevance score to each input feature for a specific prediction, indicating how much each feature contributed to that particular prediction.

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2 Global interpretability

Global interpretability refers to explaining the model's behavior for the entire dataset, task, or domain. It can help you gain insights into the model's logic and structure, evaluate its generalization and validity, and compare it with other models. Activation maximization is a technique that generates synthetic inputs that maximize the activation of a certain neuron or layer in the model, allowing you to visualize what the model has learned and what features it is looking for. Dimensionality reduction reduces the complexity and size of the model's hidden representations by projecting them into a lower-dimensional space, helping to cluster and analyze the model's internal states and their relations. Network dissection labels and quantifies the semantic concepts that each neuron or layer in the model detects, so you can understand its hierarchy and compositionality of knowledge.

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3 Why both are important

Local and global interpretability are both necessary for explaining ANNs, with local interpretability providing detailed information about the model's decisions and global interpretability offering general and holistic information about its learning. This can help achieve various goals, such as building confidence in the model, identifying and correcting errors and biases, and optimizing its performance. Interpretability is a crucial and challenging aspect of ANNs, and requires using various methods and techniques to gain a better understanding of the model's behavior. This will make ANNs more reliable, transparent, and useful.

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Kai Bitterschulte

Principal Solutions Engineer EMEA at Cloudflare (NYSE:NET) - The Connectivity Cloud ??
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Local interpretability in ANNs focuses on explaining individual predictions, while global interpretability uncovers overall model behavior. Both are crucial for trust and transparency in AI.

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4 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?

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What is the difference between local and global interpretability in ANNs?

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1 Local interpretability

2 Global interpretability

3 Why both are important

4 Here’s what else to consider

Machine Learning

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What is the difference between local and global interpretability in ANNs?

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1 Local interpretability

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3 Why both are important

4 Here’s what else to consider

Machine Learning

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