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.
Resources:
9 min read
The Drone Blockade: Airports Grapple with A Growing Threat
by Nick Sherman
An uninvited buzz of drones in the air is forcing airports worldwide to rethink their approach to security. The implications are serious, impacting...
13 min read
Defence and National Security AI Strategies - The Global Landscape
by Nick Sherman
The Ukraine-Russia war has clearly demonstrated AI’s effectiveness on the battlefield. Across the globe, countries are devising strategies to...
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