# Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters

arXiv: https://arxiv.org/abs/2607.17548 · HTML: https://mlsanity.com/paper/2607.17548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 79 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations](https://mlsanity.com/paper/2012.03058/) (2020)
- [The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](https://mlsanity.com/paper/2008.09100/) (2020)
- [Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience](https://mlsanity.com/paper/2001.09219/) (2020)
- [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)
- [A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems](https://mlsanity.com/paper/1811.11839/) (2018)
- [Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition](https://mlsanity.com/paper/2005.02335/) (2020)
- ["Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users](https://mlsanity.com/paper/1805.08578/) (2018)
- [In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems](https://mlsanity.com/paper/2004.00762/) (2020)

## Abstract (arXiv)

While ML can produce complex models beyond those that a human could produce manually, incorporating human input can often improve performance beyond purely data-driven models. While this feedback could come from system designers or domain experts, in many cases, the end users who regularly use the system will naturally develop an understanding of its flaws and desire the ability to change the system's behavior based on their knowledge. While soliciting feedback from end users can result in significant model improvement over time, introducing these feedback techniques can also affect several human factors-such as trust or perception of system accuracy-that are not yet fully understood and have different effects reported in the existing literature. Therefore, we sought to build on the existing research to further explore how the act of providing feedback can affect user understanding of an intelligent system and its accuracy in different contexts. We present three controlled experiments that study the effects of interactive feedback collections on user impressions in domains with objective and subjective feedback. The results show that in a context where there is an objectively correct answer, providing HITL feedback lowered both participants' trust in the system and their perception of system accuracy, regardless of whether the system accuracy improved in response to their feedback. However, when the feedback being provided involved subjective opinion, no such negative bias was observed. Furthermore, in the objective context, participants distrusted the system over time, whereas participants in the subjective context mistrusted the system over time. These results highlight the importance of considering the effects of allowing different types of end-user feedback on user trust when designing intelligent systems.