The term model fairness is closely linked to the areas of artificial intelligence, big data and smart data as well as digital society. Model fairness describes how fair and balanced an AI model makes decisions, for example when granting loans or selecting applicants.
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. Nobody should be favoured or disadvantaged just because certain data suggests it.
Unfortunately, AI models can adopt biases if they are trained with incorrect or unbalanced data. Model fairness is therefore an important issue: companies must regularly check whether their systems work fairly and do not systematically exclude or disadvantage certain groups.
With Model Fairness, companies create trust and ensure that digital solutions are used fairly and responsibly. This fairness is particularly important for big data and artificial intelligence so that everyone has the same opportunities.
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