Global & Local Interpretations

Taking both approaches allows for a comprehensive understanding of model decisions, facilitating better decision-making and responsible AI use.

In a broader sense, machine cognition would encompass different capabilities along with other aspects of artificial intelligence that aim to mimic or complement human cognitive functions. These areas highlight how machine learning is being used to replicate and enhance cognitive processes like decision-making and predictive reasoning.

Global and local interpretation are two key concepts in the field of machine learning interpretability, focusing on understanding how models make predictions. Here's a detailed comparison of both:

Global/local: Interpretation: Global interpretation refers to understanding the overall behavior and decision-making process of a model across the entire dataset. It aims to provide insights into how different features interact with each other and influence predictions. Local Interpretation: Local interpretation focuses on explaining individual predictions made by the model. It aims to clarify why a specific decision was made for a particular instance, providing insights into the features that influenced that decision.

Characteristics of Global Interpretation: Holistic View offers a summary of how the model behaves as a whole, rather than focusing on specific instances. Feature Importance: Identify which features are most influential in determining predictions across the entire dataset. This can help in understanding the model’s general tendencies. Model-Specific Insights: Some techniques are designed specifically for certain types of models (decision trees or linear models).

Techniques Global Interpretation:

-Feature Importance Scores: Techniques like permutation importance or mean decrease impurity in decision trees help assess which features contribute most to the model’s predictions.

-Partial Dependence Plots (PDPs): Visualize the relationship between a feature and the predicted outcome, while averaging out the effects of other features.

-Global Surrogate Models: A simpler, interpretable model (like a decision tree) is trained to approximate the predictions of a more complex model, providing insights into its global behavior.

Applications of Global Interpretation:

-Understanding how a model is likely to perform in various scenarios.

-Identifying and mitigating biases in model predictions.

-Communicating model behavior to stakeholders or regulatory bodies.

Characteristics of Local Interpretation: Instance-Specific: Provide explanations tailored to individual data points, rather than the model's overall behavior. Detailed Insights: Highlight the contribution of each feature to the prediction for a specific instance, which can vary significantly from the global model behavior.

Techniques of Local Interpretation:

-LIME (Local Interpretable Model-agnostic Explanations): Create a local linear approximation of the model around the instance being examined, allowing for feature contributions to be assessed.

-Counterfactual Explanations: Provide insights by showing how the feature values would need to change for a different prediction to occur.

-Individual Conditional Expectation (ICE) Plots: Show how predictions change for an individual instance as each feature's value varies.

Applications of Local Interpretation: Understanding specific predictions in high-stakes domains where individual decisions must be justified. Providing actionable insights for users; building trust in AI systems by clarifying specific decisions.

Both global and local interpretations are essential for understanding machine learning models. Global interpretation helps stakeholders appreciate the model's overall behavior and fairness, while local interpretation provides transparency for individual predictions, fostering trust and accountability in AI systems. Taking both approaches allows for a comprehensive understanding of model decisions, facilitating better decision-making and responsible AI use.