Definition

Explainability is about making AI decisions easy to understand. It shows why a model picked one answer over another, which builds trust and helps fix problems. If users can’t see how AI thinks, they’re less likely to trust or use it.

Example

An AI that explains why it denied a loan helps users understand the decision and fix mistakes.

How It’s Used in AI

Used in healthcare, finance, legal tech, and any field where AI decisions impact people. Explainability helps users catch errors, improve fairness, and follow laws that require transparency. It's also key for debugging and improving models.

Brief History

Explainability became more important in the late 2010s, especially with laws like the EU’s GDPR requiring a “right to explanation.” Research into tools like SHAP and LIME helped open up black-box models.

Key Tools or Models

Popular tools include SHAP, LIME, What-If Tool (Google), and Explainable Boosting Machines (EBMs). Some AI models like Claude and GPT-4 now include features that explain their thinking in plain language.

Pro Tip

The more high-stakes the AI use case, the more explainability matters. Always ask: “Can a human understand this decision?”