What is the term for systematic discrimination in AI decision-making influenced by prejudiced data?

The term that accurately describes systematic discrimination in AI decision-making influenced by prejudiced data is algorithmic bias. This concept refers to the ways in which an algorithm can produce results that are systematically prejudiced due to erroneous assumptions in the machine learning process. These biases can arise from several factors, including biased training data, flawed data collection methods, or even the design of the algorithms themselves.

Algorithmic bias is particularly critical because it can lead to unfair outcomes in various applications, such as hiring, law enforcement, and loan approvals, where biased data might reinforce stereotypes or unequal treatment of certain groups. When algorithms are trained on data that reflect societal biases, they can perpetuate and even exacerbate these biases in the decision-making process. Understanding and identifying algorithmic bias is crucial for creating fair and equitable AI systems.