The term \u201eAI model quality assurance\u201c originates from the fields of Artificial Intelligence, automation, and Big Data and Smart Data. It is about ensuring that Artificial Intelligence (AI) functions reliably, accurately, and fairly. AI models make decisions or recognise patterns, for example, in facial recognition on smartphones or in automatic text translations on the internet.<\/p>\n
Quality assurance here means: Experts regularly check whether the AI is delivering the desired results and not making any mistakes. It is also checked that the AI does not produce biased (i.e. unfair) results. This is particularly important when AI is used in large companies, at banks, or in medicine.<\/p>\n
A clear example: A bank uses an AI model to assess creditworthiness. To ensure no one is disadvantaged, experts must constantly check that the AI judges everyone fairly. Only when quality assurance is regularly carried out does the AI work reliably. This is how secure and trustworthy applications are created, where users can rely on fair decisions.<\/p>","protected":false},"excerpt":{"rendered":"