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.
Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new…