Sampling Bias
Occurs when the sample collected is not representative of the population from which it was drawn, leading to skewed results and conclusions.
Sampling bias is a critical challenge in AI and ML, where it affects the generalizability of models if the training data does not accurately reflect the diversity of the underlying population or real-world conditions. This bias can emerge due to improper or non-random selection of data, underrepresentation of key subgroups, or even through systematic errors in data collection methods. In AI systems, this can result in biased predictions or decisions, propagating inequalities or errors in automated systems. To mitigate sampling bias, data scientists often employ strategies such as stratified sampling, augmentation of minority class data, and rigorous validation procedures to ensure more representative datasets. Understanding and addressing this bias is crucial for improving model fairness, accuracy, and deployment success in varied AI applications.
The issue of sampling bias came into prominence around the early 20th century in statistical circles and has gained significant attention in AI with the rise of data-driven models and systems in the late 2010s. As AI technologies evolved, the impact of biased training data on model outcomes, especially when deployed in sensitive contexts like law enforcement and hiring, spurred greater awareness and research into detecting and correcting sampling biases.
John Tukey, an eminent statistician known for his work in exploratory data analysis, played a significant role in highlighting the importance of proper sampling methods. More recently, key figures contributing to the understanding and mitigation of sampling bias in AI include Anupam Datta and Sorelle Friedler, who have provided crucial insights into algorithmic fairness and data ethics.