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

Source: ["Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users](<https://www.sota2.com/research/paper/68fa64601e209131cea04212>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:54:50.324Z

Authors: Stefano Teso, Kristian Kersting

Published: 2018-05-22T13:46:59.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1805.08578>)

[PDF](<https://arxiv.org/pdf/1805.08578.pdf>)