AI GLOSSARY

Interpretability

Interpretability refers to the ability to understand and explain how an AI or machine learning model makes its decisions. By providing insights into the relationship between inputs and outputs, an interpretable model openly shows its internal workings and provides information that makes it easier for users to trust, verify, and act on the model’s predictions. Interpretability is especially important in high-stakes applications where transparency is required to ensure accountability and fairness.

All Terms
A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z

Continue learning...

View Resources

13 min read
Defence and National Security AI Strategies - The Global Landscape
The Ukraine-Russia war has clearly demonstrated AI’s effectiveness on the battlefield. Across the globe, countries are devising strategies to...
5 min read
Digital Custodians for Ageing Infrastructure
The UK spends £1.5 billion each year maintaining its 100,000 bridges and structures across the road and rail networks. The average age of these...
7 min read
Bridge Inspections: 5 Challenges Every Asset Manager Faces
Inspections are the primary way we assess a bridge’s condition, but traditional methods have their limitations. AI and Machine Learning offer new...

Stay connected

News, announcements, and blogs about AI in high-stakes applications.