Bias in AI

Bias in AI refers to the systematic favoritism or prejudice that can occur in artificial intelligence systems, often due to the data they are trained on or the algorithms used. This can lead to skewed outcomes that disproportionately affect certain groups of people, resulting in unfair treatment or discrimination. Characteristics of bias in AI include its ability to perpetuate stereotypes, its presence in decision-making processes, and its potential to cause harm in real-world applications. Common use cases where bias can manifest include hiring algorithms, facial recognition systems, and credit scoring models. Addressing bias is crucial to ensure fairness, accountability, and transparency in AI systems.