The term model fairness is closely linked to the fields of Artificial Intelligence, Big Data and Smart Data, and the Digital Society. Model fairness describes how justly and equitably an AI model makes decisions, for example, in credit lending or applicant selection.
Imagine a company uses Artificial Intelligence to sort applications. Model fairness means that the system treats all applicants equally, regardless of gender, origin, or age. No one should be favoured or disadvantaged just because certain data suggests it.
Unfortunately, AI models can pick up bias if they are trained with flawed or unbalanced data. This is why model fairness is an important topic: companies must regularly check whether their systems operate fairly and do not systematically exclude or disadvantage certain groups.
With Model Fairness, companies build trust and ensure that digital solutions are used fairly and responsibly. This fairness is extremely important, especially with Big Data and Artificial Intelligence, so that all people are given equal opportunities.
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