Active learning with human heuristics: an algorithm robust to labeling bias

Abstract

Active learning enables prediction models to achieve better performance faster by adaptively querying an oracle for the labels of data points. Sometimes the oracle is a human, for example when a medical diagnosis is provided by a doctor. According to the behavioral sciences, people, because they employ heuristics, might sometimes exhibit biases in labeling. How does modeling the oracle as a human heuristic affect the performance of active learning algorithms? If there is a drop in performance, can one design active learning algorithms robust to labeling bias? The present article provides answers. We investigate two established human heuristics (fast-and-frugal tree, tallying model) combined with four active learning algorithms (entropy sampling, multi-view learning, conventional information density, and, our proposal, inverse information density) and three standard classifiers (logistic regression, random forests, support vector machines), and apply their combinations to 15 datasets where people routinely provide labels, such as health and other domains like marketing and transportation. There are two main results. First, we show that if a heuristic provides labels, the performance of active learning algorithms significantly drops, sometimes below random. Hence, it is key to design active learning algorithms that are robust to labeling bias. Our second contribution is to provide such a robust algorithm. The proposed inverse information density algorithm, which is inspired by human psychology, achieves an overall improvement of 87% over the best of the other algorithms. In conclusion, designing and benchmarking active learning algorithms can benefit from incorporating the modeling of human heuristics.