Counterfactual explainability is a term from the fields of artificial intelligence, big data and smart data, as well as digital transformation. It helps to make complex decisions made by computer models more understandable.<\/p>\n
Imagine this: 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: \u201eWhat would I have had to do differently for the outcome to have been different?\u201c For example: \u201eIf your income had been 500 Euros higher, you would have received the loan.\u201c This makes AI decisions, often perceived as black boxes, more tangible and comprehensible.<\/p>\n
Counterfactual explainability therefore helps to make critical business decisions transparent. It concretely and understandably presents alternatives. This is particularly important when companies rely on AI models whose workings are difficult to understand. This allows those responsible, customers, or users to more easily understand how they can influence results and to recognise bad decisions early on.<\/p>","protected":false},"excerpt":{"rendered":"