Explainability

Definition

Explainability is essential in building trust in AI systems by ensuring users can comprehend how decisions are made; it involves creating methods that allow stakeholders to interpret the reasoning behind predictions or classifications effectively.

When Explainability is used

LIME (Local Interpretable Model-agnostic Explanations)

Which positions need this?

Data Scientists

Problem

Lack of transparency leads to distrust among users.

Example of how Explainability is used in AI

A bank implements explainable AI techniques that allow customers to understand why their loan applications were approved or denied based on specific criteria highlighted by the model.