Description: Statistical discrimination refers to the practice of making decisions based on statistical characteristics of groups rather than evaluating individuals uniquely. This approach can be problematic as it may perpetuate biases and inequalities in artificial intelligence (AI) systems. In the context of AI, statistical discrimination can manifest when algorithms use historical data that reflects social prejudices, leading to decisions that disadvantage certain groups. For example, if an AI model is trained on data showing that a demographic group has a higher rate of payment defaults, the system may infer that all individuals from that group are equally likely to default, ignoring individual circumstances. This practice is not only ethically and morally questionable but can also have legal and reputational consequences for organizations that implement it. Statistical discrimination highlights the need for a more equitable and conscious approach in the development and implementation of AI technologies, where fairness and justice in automated decision-making are prioritized.
