Are Good Explainers Secretly Human-in-the-Loop Active Learners?
This work addresses the challenge of efficiently collecting training data in machine learning, though it appears incremental by formalizing an existing connection.
The paper tackles the problem of using explainable AI (XAI) techniques to gather training data, arguing this is equivalent to human-in-the-loop active learning. It provides a mathematical formalization and simulation-based assessment, showing initial promising results.
Explainable AI (XAI) techniques have become popular for multiple use-cases in the past few years. Here we consider its use in studying model predictions to gather additional training data. We argue that this is equivalent to Active Learning, where the query strategy involves a human-in-the-loop. We provide a mathematical approximation for the role of the human, and present a general formalization of the end-to-end workflow. This enables us to rigorously compare this use with standard Active Learning algorithms, while allowing for extensions to the workflow. An added benefit is that their utility can be assessed via simulation instead of conducting expensive user-studies. We also present some initial promising results.