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.
Edge Computing brings computing resources closer to the source of data generation, reducing latency and improving response times. By processing and analysing data locally…