To understand the decision making process of "black box" artificial intelligence systems, such as deep neural networks, researchers and practitioners rely on a field known as Explainable AI (XAI).
Since the internal computations of these models are often too complex for direct human interpretation, XAI methods function like "probes" that analyze the relationship between inputs and outputs to infer the underlying logic. This is typically achieved through post-hoc interpretability, which involves analyzing a model after it has been trained. Techniques usually fall into two categories: global interpretability, which attempts to create a simplified map of how the model works overall, and local interpretability, which explains why a specific decision was made for a single instance.
By using methods that highlight influential features (like pixels in an image or keywords in text) or by simulating "what-if" scenarios, humans can audit these systems for bias, errors, and safety without needing to understand every mathematical parameter inside the "box."
| Technique | Description | Primary Insight |
|---|---|---|
| LIME (Local Interpretable Model-agnostic Explanations) | Approximates the complex model with a simpler one (like a linear model) only around the specific data point being analyzed. | Local Justification: Reveals which specific features like a specific word or image region tipped the scale for a single prediction. |
| SHAP (SHapley Additive exPlanations) | Uses game theory to calculate the average marginal contribution of each feature across all possible combinations of inputs. | Feature Attribution: Provides a mathematically consistent "credit score" for each feature, showing exactly how much it pushed the prediction up or down. |
| Counterfactual Explanations | Identifies the minimal change required in the input data to flip the model's decision like "If income increased by $500, the loan would be approved." | Actionability: Helps users understand what needs to change to achieve a different outcome, useful for feedback and recourse. |
| Global Surrogate Models | Trains an interpretable model (like a Decision Tree) to mimic the predictions of the black box model as closely as possible. | General Logic: Offers a high-level flowchart of the black box’s decision boundaries, making the overall strategy easier to visualize. |
| Saliency Maps (Pixel Attribution) | Generates a heat map for image-based models, highlighting pixels that had the strongest gradient impact on the final classification. | Visual Focus: Shows "where the AI is looking" in an image, helping to detect if the model is focusing on relevant objects or background noise. |
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