The term Model Interpretability is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes how understandable the results and decisions of a complex AI or data model are for humans. Simply put: Model Interpretability ensures that we can understand why an artificial intelligence, for example, gives a specific rating or recommendation.
This becomes particularly important when decisions have significant impacts on people – for example, in credit lending, healthcare diagnoses, or personalised advertising. An interpretable model helps those responsible to examine the „logic“ behind a decision or even to identify errors.
Imagine an automated system approving or rejecting loan applicants. Without model interpretability, it would be impossible to understand why someone was rejected. With an interpretable model, you could easily understand the reason, for example, because the income was too low, or because there were past repayment issues.
Model interpretability therefore contributes to creating transparency and trust in digital decisions – an important building block for the widespread acceptance of artificial intelligence in business and society.
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