# Interpretable Active Learning

arXiv: https://arxiv.org/abs/1708.00049 · HTML: https://mlsanity.com/paper/1708.00049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs](https://mlsanity.com/paper/2601.03087/) (2026)
- [A Turing Test for Transparency](https://mlsanity.com/paper/2106.11394/) (2021)
- [Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities](https://mlsanity.com/paper/2509.02926/) (2025)
- [Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up](https://mlsanity.com/paper/2110.03923/) (2021)

## Abstract (arXiv)

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. We demonstrate how LIME can be used to generate locally faithful explanations for an active learning strategy, and how these explanations can be used to understand how different models and datasets explore a problem space over time. In order to quantify the per-subgroup differences in how an active learning strategy queries spatial regions, we introduce a notion of uncertainty bias (based on disparate impact) to measure the discrepancy in the confidence for a model's predictions between one subgroup and another. Using the uncertainty bias measure, we show that our query explanations accurately reflect the subgroup focus of the active learning queries, allowing for an interpretable explanation of what is being learned as points with similar sources of uncertainty have their uncertainty bias resolved. We demonstrate that this technique can be applied to track uncertainty bias over user-defined clusters or automatically generated clusters based on the source of uncertainty.