Algorithmic Discrimination

Algorithmic discrimination refers to the bias that can occur when algorithms produce unfair or prejudiced outcomes based on certain attributes such as race, gender, or socio-economic status. This phenomenon arises from the data used to train machine learning models, which may reflect existing societal biases. As a result, the automated decisions made by these algorithms can perpetuate inequality and discrimination in various sectors, including hiring, law enforcement, and lending. Addressing algorithmic discrimination involves implementing fairness-aware algorithms and ensuring diverse and representative training datasets.