The term \u201emodel cards for ML\u201c is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. Model cards help to make machine learning models (ML models) more understandable and transparent for developers, decision-makers, and users.<\/p>\n
A model card is rather like a patient information leaflet for an AI model. It contains important information about how the model works, what it's intended for, what data it uses, and where its limitations lie. This allows companies or authorities to better assess whether a model is suitable for their purpose and where problems might arise.<\/p>\n
A clear example: A city administration uses an AI model to predict traffic flow. The associated model card explains that the model was primarily trained on data from main roads. This allows the responsible parties to know that statements for minor roads may be less reliable. <\/p>\n
Model cards for ML promote fairness and transparency, as they help to identify sources of error and unexpected effects early on. Especially in the context of digital transformation, they are an important tool for building trust in artificial intelligence.<\/p>","protected":false},"excerpt":{"rendered":"