Google’s newly-launched What-If Tool gives users the ability to analyze a machine learning (ML) model without having to write code for it.

Given the increasing scrutiny leveled on the opaque nature of machine learning, the What-If tool will offer greater accessibility to parties interested in examining and evaluating ML models.

The Google AI Blog explains:

The What-If Tool has a large set of features, including visualizing your dataset automatically using Facets, the ability to manually edit examples from your dataset and see the effect of those changes, and automatic generation of partial dependence plots which show how the model’s predictions change as any single feature is changed.

To illustrate the capabilities of the What-If Tool, we’ve released a set of demos using pre-trained models:

• Detecting misclassifications: A multiclass classification model, which predicts plant type from four measurements of a flower from the plant. The tool is helpful in showing the decision boundary of the model and what causes misclassifications. This model is trained with the UCI iris dataset.

• Assessing fairness in binary classification models: The image classification model for smile detection mentioned above. The tool is helpful in assessing algorithmic fairness across different subgroups. The model was purposefully trained without providing any examples from a specific subset of the population, in order to show how the tool can help uncover such biases in models. Assessing fairness requires careful consideration of the overall context — but this is a useful quantitative starting point.

• Investigating model performance across different subgroups: A regression model that predicts a subject’s age from census information. The tool is helpful in showing relative performance of the model across subgroups and how the different features individually affect the prediction. This model is trained with the UCI census dataset.