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The What-If Tool: Code-Free Probing of Machine Learning Models

Tuesday, September 11, 2018

Posted by James Wexler, Software Engineer, Google AIHow would changes to a datapoint affect my model’s prediction? Does it perform differently for various groups–for example, historically marginalized people? How diverse is the dataset I am testing my model on?Google AI PAIR initiativeWhat-If ToolTensorBoard

The What-If Tool, showing a set of 250 face pictures and their results from a model that detects smiles.

Facets

Exploring what-if scenarios on a datapoint.

CounterfactualsUCI census dataset

Comparing counterfactuals.

Analysis of Performance and Algorithmic Fairnessnumerical fairness criteriaCelebA datasetROC curveconfusion matrixequal opportunity

Comparing the performance of two slices of data on a smile detection model, with their classification thresholds set to satisfy the “equal opportunity” constraint.

Demos

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.

What-If in PracticeAcknowledgmentsThe What-If Tool was a collaborative effort, with UX design by Mahima Pushkarna, Facets updates by Jimbo Wilson, and input from many others. We would like to thank the Google teams that piloted the tool and provided valuable feedback and the TensorBoard team for all their help.