Bias in AI refers to systematic errors or prejudices that can occur within AI systems due to biased training data, faulty algorithms, or human biases. Addressing bias in AI is crucial for ensuring fairness, inclusiveness, and ethical practices in AI applications.
Feature Extraction refers to the process of identifying and selecting the most relevant features from raw data to enhance AI model performance. By extracting…