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.
Ensemble Learning involves combining multiple Machine Learning models to achieve superior performance and accuracy. By leveraging the “wisdom of the crowd,” Ensemble Learning mitigates…