Interpretability Measure

An Interpretability Measure is a measure that quantifies how easily a human can understand the decisions or predictions made by a machine learning model or algorithm.

Context:
- It can (typically) assess the Model Interpretability of a black-box model by evaluating its transparency and simplicity.
- It can (often) provide insights into the Decision-Making Process of complex models, ensuring they align with human reasoning.
- It can range from being a quantitative metric to being a qualitative assessment.
- It can help in comparing the interpretability of different machine learning models.
- It can assist in regulatory compliance by ensuring models meet transparency standards.
- It can support debugging and improving model performance by identifying which parts of the model are most and least interpretable.
- It can enhance user trust in AI systems by providing understandable and actionable insights.
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Example(s):
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Counter-Example(s):
- Explainability, which focuses on providing a clear and understandable explanation of how a model works or makes decisions, rather than measuring the ease of understanding.
- Predictive Accuracy, which measures the correctness of a model's predictions without considering how understandable the model is.
- Performance Metrics, such as precision, recall, or F1 score, which evaluate a model's effectiveness rather than its interpretability.
See: Model Interpretability, Mathematical Logic, Interpretation, Effectiveness Measure, Efficiency Measure.

References

(Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/interpretability Retrieved:2015-12-25.
- In mathematical logic, interpretability is a relation between formal theories that expresses the possibility of interpreting or translating one into the other.