Preprocessing bias mitigation
Preprocessing bias mitigation involves techniques used to reduce bias in datasets before they are used for machine learning. These methods aim to enhance fairness and accuracy in AI systems.
fairness
Individual fairness
Individual fairness is a concept in ethics and machine learning that emphasizes the fair treatment of individuals by ensuring that similar individuals receive similar outcomes.
Contrastive fairness
Contrastive fairness is a framework in ethical decision-making and artificial intelligence aimed at ensuring equitable treatment across different groups.
Demographic parity
Demographic parity refers to the principle that outcomes in decision-making processes should reflect the demographic composition of the population.
Postprocessing bias mitigation
Postprocessing bias mitigation refers to techniques aimed at reducing bias in machine learning model outputs after the training phase, ensuring fairer results.
Fairness metrics (machine learning)
Fairness metrics in machine learning are measures used to assess the fairness of algorithms, ensuring equitable treatment across different demographic groups.