# "Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users

arXiv: https://arxiv.org/abs/1805.08578 · HTML: https://mlsanity.com/paper/1805.08578/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

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

- [Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy](https://mlsanity.com/paper/2008.12735/) (2020)
- [Class Introspection: A Novel Technique for Detecting Unlabeled Subclasses by Leveraging Classifier Explainability Methods](https://mlsanity.com/paper/2107.01657/) (2021)
- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)
- [Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters](https://mlsanity.com/paper/2607.17548/) (2026)

## Abstract (arXiv)

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind queries and predictions is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning: in each step, the learner explains its interactive query to the user, and she queries of any active classifier for visualizing explanations of the corresponding predictions. We demonstrate that this can boost the predictive and explanatory powers of and the trust into the learned model, using text (e.g. SVMs) and image classification (e.g. neural networks) experiments as well as a user study.