Model Fairness
Model Fairness is the practice of evaluating and improving machine learning systems to reduce unjustified differences in how they treat individuals or groups. It aims to ensure that model predictions and decisions are appropriate and equitable across relevant populations.
What is Model Fairness?
Model fairness involves examining how a model performs across different groups and identifying disparities in outcomes, error rates, or access to opportunities. Fairness can be assessed using different measures depending on the application, such as demographic parity, equalized odds, or equal opportunity.
Why is Model Fairness Important?
Unfair models can reinforce or amplify biases present in training data or decision-making processes. Evaluating fairness helps organizations identify potential discrimination, improve model reliability, support responsible AI practices, and meet applicable ethical or regulatory requirements.
Common use cases
Model fairness is commonly evaluated in hiring, lending, healthcare, insurance, education, employment, facial recognition, and other applications where AI decisions can significantly affect individuals or groups.