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:
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Machine Learning Types and Their Infrastructure Use Cases
by Kimberly Joly
AI and Machine learning is a complex field with numerous models and varied techniques. Understanding these different types and the problems that each...
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The Business of AI in UK Defence and National Security
by Al Bowman
While the technical aspects of an AI system are important in Defence and National Security, understanding and addressing AI business considerations...
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