explainability Definition
the ability of a machine learning model or system to provide clear and understandable reasons for its outputs or decisions.
Using explainability: Examples
Take a moment to familiarize yourself with how "explainability" can be used in various situations through the following examples!
Example
The explainability of the AI system is crucial for gaining user trust.
Example
The lack of explainability in the algorithm's decision-making process led to controversy.
Example
Explainability is becoming increasingly important as AI is integrated into various industries.
explainability Synonyms and Antonyms
Synonyms for explainability
Phrases with explainability
the ability of a machine learning model to provide clear and understandable reasons for its outputs or decisions
Example
Model explainability is essential for ensuring that the AI system is making ethical and unbiased decisions.
an AI system that can provide clear and understandable reasons for its outputs or decisions
Example
Explainable AI is becoming more popular as companies seek to build trust with their customers.
the difference between what a machine learning model can explain and what humans need to understand
Example
The explainability gap is a major challenge in building trustworthy AI systems.
Summary: explainability in Brief
The term 'explainability' [ɪksˌpleɪnəˈbɪlɪti] refers to the ability of a machine learning model or system to provide clear and understandable reasons for its outputs or decisions. It is crucial for gaining user trust and ensuring ethical and unbiased decisions. The concept extends into phrases like 'model explainability' and 'explainable AI,' which are becoming increasingly popular as companies seek to build trust with their customers.
