Expose data biases
The traditional method of evaluating the trustworthiness of a model’s performance is to look at calculated metrics such as accuracy, recall, precision, root mean squared error (RSME), mean absolute error (MAE), or R2 depending on the type of use-case you have (for example, classification or regression). Data scientists and AI developers can also measure confidence levels for areas the model correctly predicted or the frequency of making correct predictions. You can also try to isolate your test data in separate cohorts to observe and compare how the model performs with some groups vs. others. However, all of these techniques ignore a major blind spot: the underlying data.
Data can be overrepresented in some cases and underrepresented in others. This might lead to data biases, causing the model to have fairness, inclusiveness, safety, and/or reliability issues.
The Responsible AI dashboard includes a data analysis component that enables users to explore and understand the dataset distributions and statistics. It provides an interactive user interface (UI) to enable users to visualize datasets based on the predicted and actual outcomes, error groups, and specific features. This is useful for ML professionals to be able to quickly debug and identify issues of data over- and under-representation and to see how data is clustered in the dataset. As a result, they can understand the root cause of errors and any fairness issues introduced via data imbalances or lack of representation of a particular data group.
With the data analysis component, a Table view pane shows you a table view of your raw dataset with all the features as well as the true outcome vs predicted. In addition, the Chart view panel shows you aggregate and individual plots of datapoints. You can analyze data statistics along the x-axis and y-axis by using filters such as predicted outcome, dataset features, and error groups. This view helps you understand overrepresentation and underrepresentation in your dataset.