Counterfactual explainability is a term used in the fields of artificial intelligence, big data, smart data and digital transformation. It helps to make complex decisions made by computer models easier to understand.
Imagine the following: An AI decides whether someone gets a loan or not. For many people, it remains unclear why exactly they were rejected. This is where counterfactual explainability comes in. It answers the question: "What would I have had to do differently so that the result would have been different?" For example: "If your income had been 500 euros higher, you would have got the loan." This makes AI decisions, which are often perceived as a black box, more tangible and comprehensible.
Counterfactual explainability therefore helps to make critical business decisions transparent. It shows concrete and understandable alternatives. This is particularly important when companies rely on AI models that are difficult to understand how they work. This makes it easier for managers, customers or users to understand how they can influence results and recognise wrong decisions at an early stage.
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