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Artificial intelligence (AI) refers to the capability of a computer to perform cognitive tasks that are typically associated with human intelligence such as learning, reasoning, and problem-solving. Due to the success of machine learning, especially foundation models (e.g. , ChatGPT), the field of AI is currently experiencing rapid growth and has the potential to revolutionize many aspects of our lives, from health care to finance, criminal justice to edu- cation. With the increasing deployment of AI systems in these fields, ensuring their ability to generalize to new, unseen data has become crucial for optimal performance and practical utility in real-world applications. Furthermore, it is crucial that the decisions made by these systems are transparent and explainable for greater accountability and trustworthiness. Machine Learning serves as the foundational backbone and a critical component in the field of AI. It centers on the development and refinement of models that learn from large amounts of data, identify patterns, predict outcomes, and make decisions based on input data. Generalization, the ability of an ML model to perform well on new and previously unseen data, is crucial for high performance and real-world applicability. This ability enables an AI system to perform robustly in the presence of changes in the data distribution. When amodel lacks strong generalization capabilities, it tends to overfit the training data by capturing the spurious correlations and statistical noise of the training data instead of the underlying patterns and rules. In such cases, the model may perform poorly on new data and result in poor performance and low applicability in real-world scenarios. Therefore, ensuring that AI systems can generalize well is a critical challenge in the development and deployment of these systems.