In AI and ML, simplicity bias refers to the inclination of models, especially those relying on algorithms like decision trees or neural networks, to prefer simple hypotheses or patterns over complex ones when making decisions or predictions. This bias is often rooted in the principle of Occam's Razor, which suggests that among competing hypotheses, the one with the fewest assumptions should be selected. It plays a crucial role in regularization techniques used in ML to prevent overfitting, where the model captures noise and irrelevant details rather than the underlying data distribution. However, an excessive preference for simplicity can lead models to overlook nuanced patterns, resulting in underfitting and reduced performance on complex datasets.
The concept of simplicity bias has been indirectly recognized since the early days of AI and formalized in various forms throughout the 1980s and 1990s, particularly gaining attention with the rise of more complex model architectures in the 2000s when balancing model complexity became a significant focus.
Key contributors to formalizing the notion of simplicity bias within AI include pioneers of statistical learning theory such as Vladimir Vapnik and others advancing concepts in model selection and regularization techniques, who emphasized the importance of balancing complexity and simplicity in model development.