Simplicity Bias

Simplicity bias, the tendency of machine learning models to favor simpler solutions over more complex, potentially more accurate ones, is a significant area of research. Current investigations focus on understanding this bias in various architectures, including neural networks (especially two-layer ReLU networks and transformers) and its impact on generalization, robustness, and fairness, often employing techniques like Sharpness-Aware Minimization or regularization strategies to mitigate its effects. Addressing simplicity bias is crucial for improving model performance, particularly in out-of-distribution settings and for preventing the amplification of existing biases in data, leading to more reliable and equitable AI systems.