Despite the remarkable advancements towards generalizability and autonomy in AI systems, persistent challenges such as spurious correlations and shortcut learning continue to hinder the robustness, reliability, and ethical deployment of machine learning systems. These challenges arise from the statistical nature of machine learning algorithms and their implicit or inductive biases at all stages, including data preprocessing, architectures, and optimization. As a result, models rely on spurious patterns rather than understanding underlying causal relationships, making them vulnerable to failure in real-world scenarios where data distributions involve under-represented groups or minority populations. The foundational nature and widespread occurrence of reliance on spurious correlations and shortcut learning make it an important research topic and a gateway to understanding how deep models learn patterns and the underlying mechanisms responsible for their effectiveness and generalization. This workshop aims to foster a collaborative community to address these critical issues by bringing together experts from diverse fields and pushing the boundaries of current research. We will focus on promoting three key avenues: (i) the development of comprehensive evaluation benchmarks and the exploration of under-examined facets of the problem, (ii) the creation of novel solutions for building robust models that effectively tackle spurious correlations in real-world applications, and (iii) shedding light on lesser-explored aspects to deepen our understanding of the nature of these phenomena.