The term \u201equality metrics for AI explainability\u201c comes from the fields of artificial intelligence and digital transformation. It describes methods used to measure how well artificial intelligence (AI) makes its decisions comprehensible to humans.<\/p>\n
Quality metrics help to evaluate the \u201eexplainability\u201c of an AI. This means they answer questions such as: Do I understand why the AI made the decision it did? Can I understand the sequence of its decision-making steps? This is particularly important when AI is used in sensitive areas such as medicine, finance or industry.<\/p>\n
An example: An AI checks loan applications and rejects an application. With good quality metrics for AI explainability, a clerk can see exactly what data led to the rejection - for example, income, credit history or current debts. Without this kind of transparency, it would be very difficult to understand the decision or answer customer questions.<\/p>\n
In short: quality metrics for AI explainability ensure that people can trust AI and check its work. This aspect is playing an increasingly important role, especially in the digital transformation of companies.<\/p>","protected":false},"excerpt":{"rendered":"