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This article discusses the concept of 'Blindspot' in machine learning, where models can fail to generalize to real-world data due to biases in the training data. It explores techniques to identify and mitigate these blindspots, such as adversarial training and causal reasoning, in order to improve the reliability and fairness of AI systems.