AI should not be a black box. With Workbench’s Explainable AI capabilities, you can understand exactly how your models make decisions, bringing transparency and trust to every prediction. Whether you’re building internal tools or customer-facing applications, clear explanations foster confidence and accountability.
Workbench provides both global explanations that reveal how features influence the model overall, and local explanations that break down the contribution of each feature in individual predictions. With these insights, users can validate models, debug issues, and communicate outcomes clearly to stakeholders. Because AI isn’t truly powerful until it’s explainable.
Workbench empowers users with counterfactual explanations, offering a unique way to understand AI model decisions by exploring what small changes in input could have led to a different outcome. This makes AI not only more transparent, but also actionable.
For example, if a loan application is rejected by a predictive model, Workbench can show what minimal changes (e.g., higher income or lower credit usage) would have resulted in an approval. These insights help users, analysts, or customers understand how to influence results, improve decision-making, and create more fair and responsible AI systems.
