Fairness-aware classification

Fairness-aware classification involves designing algorithms that categorize data while actively considering and reducing biases related to sensitive factors like race, gender, or age. The goal is to ensure that the system makes decisions that are equitable and do not unfairly favor or discriminate against any group. By doing so, these models promote more just outcomes, helping to prevent discrimination in areas like hiring, lending, or law enforcement. Essentially, fairness-aware classification seeks to balance accuracy with social responsibility by making machine learning decisions fairer for all individuals.