Can AutoML tools explain their results?

AutoML tools can explain their results, but the depth and clarity of explanations depend on the specific tool and underlying algorithms. Most modern AutoML platforms include basic interpretability features, such as feature importance scores, which show which input variables most influenced predictions. For example, tools like Google’s AutoML Tables or H2O.ai’s Driverless AI automatically generate charts and metrics that highlight key factors in a model’s decisions. These explanations are often presented through simple visualizations, like bar graphs ranking features by their impact. However, these tools may not provide granular details about how individual predictions are made, especially for complex models like neural networks.

The level of explainability also varies by model type. AutoML systems that use inherently interpretable algorithms—such as linear regression or decision trees—can more easily produce human-readable rules or coefficients. For instance, a decision tree-based AutoML tool might output a flowchart-like structure showing how input values split at each node to reach a conclusion. In contrast, models like gradient-boosted ensembles or deep learning models, while often more accurate, are harder to interpret. Tools like Auto-sklearn or TPOT might offer partial dependence plots or SHAP (SHapley Additive exPlanations) values to approximate feature effects, but these are post-hoc interpretations rather than direct explanations of the model’s logic.

Developers can enhance AutoML explainability by integrating external libraries or custom code. For example, using LIME (Local Interpretable Model-agnostic Explanations) alongside an AutoML-generated model can help explain individual predictions by approximating complex models with simpler, local models. Some AutoML frameworks, like DataRobot, allow users to toggle between different model types to balance accuracy and interpretability. However, the responsibility often falls on the user to validate explanations and ensure they align with domain knowledge. While AutoML reduces manual effort in model building, practitioners must still critically assess whether the provided explanations are sufficient for their use case, particularly in regulated industries where auditability is required.