A non-technical guide to understanding explainability of AI models

Machine learning interpretability and the ability to explain model predictions have become critical requirements for AI projects, particularly as stakeholders need to understand how models arrive at their decisions.

Core concept introduction: SHAP (SHapley Additive exPlanations) provides a mathematical framework for breaking down machine learning predictions into individual contributions from each input variable, making complex models more transparent and interpretable.

• SHAP can be applied to any machine learning model after training, making it a versatile tool for model interpretation
• For each data point, SHAP calculates how much each feature contributes to pushing the prediction above or below the baseline
• The method works with both regression models (predicting continuous values) and classification models (predicting yes/no outcomes)

Technical framework: SHAP represents a model’s prediction as the sum of a base value plus individual SHAP values for each feature.

• For regression models, the base value is the mean of the target variable
• For classification models, the base value represents the prevalence of the positive class
• The sum of all SHAP values plus the base value exactly equals the model’s prediction

Key applications: SHAP serves multiple important purposes in machine learning workflows.

• Validates model behavior by confirming whether the model relies on features that domain experts would expect to be important
• Helps satisfy regulatory requirements for AI explanations
• Can identify potential data issues when models show unexpectedly strong relationships between features
• Assists in generating new hypotheses about relationships in the data

Important limitations: Understanding SHAP’s constraints is crucial for proper interpretation.

• SHAP shows correlations within the model but does not prove causation
• The analysis only reflects relationships present in the training data, not necessarily real-world relationships
• Decision-makers should be cautious about viewing SHAP values as direct dials for manipulating outcomes

Analysis types: SHAP provides both local and global interpretability through various visualization techniques.

• Local interpretability explains individual predictions through waterfall and force plots
• Global interpretability examines model behavior across the entire dataset using bar plots, beeswarm plots, and dependence plots
• These visualizations help communicate complex model behavior to non-technical stakeholders

Looking ahead: While SHAP represents a significant advancement in model interpretability, its effective use requires careful consideration of how to present results and acknowledge limitations. As machine learning continues to be deployed in critical applications, tools like SHAP will become increasingly important for building trust and understanding in AI systems.