Frequently Asked Questions

Explainable AI means you can show regulators and customers exactly how your algorithms make decisions—not just the outcome, but the reasoning behind it. Instead of a black box that says "loan denied," you can document which factors influenced the decision and why they matter under your compliance framework.

A thorough AI explainability audit usually takes 4–6 weeks depending on the complexity of your systems and how much documentation already exists. We start with an initial assessment of your models, then work through feature importance analysis, bias testing, and documentation gaps. The timeline includes time for your team to gather model details and test results with us.

Not necessarily. Most existing models can be made transparent and auditable through better documentation, explainability techniques, and governance structures—without replacing the underlying algorithm. In some cases, we might recommend adjustments to improve fairness or reduce bias, but wholesale rebuilds are rarely the first step.

With proper documentation and explainability frameworks in place, you'll be ready. You can walk them through your decision logic, show how features influence outcomes, demonstrate your fairness testing, and present your governance structure. We help you create audit-ready documentation so you're not scrambling when questions come.

We work with any AI system that influences customer decisions or carries regulatory risk—credit scoring, underwriting, fraud detection, customer segmentation, and risk assessment models. The principles of transparency and auditability apply across all of them.

We provide structured templates and guidance for documenting your AI systems in ways auditors understand: model purpose and scope, training data details, decision logic mapping, feature importance analysis, bias and fairness test results, and governance processes. Think of it as creating a clear, auditable record of how your algorithms work and why you trust them.