Responsible AI Toolbox overview
One of the biggest challenges we face in data science is understanding what the model does. For example, if the algorithms we use are all black boxes, it’s not that easy to know how the decisions are made. To discern how our algorithms make decisions, we can make use of responsible AI. This will give us the opportunity to explain the model’s decisions, find the features that contribute to the prediction, do error analysis on the dataset, and also ensure fairness in the dataset.
Microsoft recently developed a Responsible AI Toolbox that encompasses interpretability, fairness, counterfactual analysis, and causal decision-making through three dashboards: a fairness dashboard, an error analysis dashboard, and an interpretability dashboard.
Dashboards simplify the user interface (UI) by bringing all the toolkit output into one UI. Before the toolbox, it was hard because we needed to download a separate library and build code for each of...
