Yes, AutoML (Automated Machine Learning) can generate interpretable machine learning models, but the extent depends on the framework, configuration, and the types of models it prioritizes. AutoML tools automate tasks like algorithm selection, hyperparameter tuning, and feature engineering, and many include options to prioritize simpler, more transparent models. For example, platforms like Google’s AutoML Tables or H2O’s Driverless AI allow users to specify constraints that favor interpretable algorithms such as linear regression, decision trees, or rule-based models. These models provide clear insights into how input features influence predictions, making them easier to audit and explain compared to “black-box” models like deep neural networks.
However, there’s a trade-off between interpretability and performance. AutoML systems often default to optimizing for accuracy, which can lead to complex models like gradient-boosted trees or ensembles. To address this, some tools offer post-hoc interpretation features. For instance, AutoML frameworks might integrate techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to explain predictions from any model type. Tools like DataRobot automatically generate feature importance scores, partial dependence plots, or decision tree visualizations alongside model training. This allows developers to use high-performing models while still gaining insights into their behavior, even if the underlying model isn’t inherently interpretable.
The key limitation is that interpretability isn’t always guaranteed by default. Developers must actively configure AutoML pipelines to prioritize interpretable models or use built-in explanation tools. For example, setting a tool like TPOT (Tree-based Pipeline Optimization Tool) to exclude neural networks or enforce depth limits on decision trees can steer the search toward simpler structures. Additionally, domain-specific AutoML solutions, such as those in healthcare or finance, often bake in interpretability requirements due to regulatory needs. In summary, AutoML can produce interpretable models, but achieving this requires deliberate setup and leveraging the tool’s interpretability-focused features rather than relying solely on automation.