# Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy

arXiv: https://arxiv.org/abs/2008.12735 · HTML: https://mlsanity.com/paper/2008.12735/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 28 Aug 2020.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 7 of 7)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [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)
- [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)

## Built on by (most cited first, 20 of 20)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations](https://mlsanity.com/paper/2402.00491/) (2024)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform](https://mlsanity.com/paper/2310.02063/) (2023)
- [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026)
- [An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs](https://mlsanity.com/paper/2508.21024/) (2025)
- [UpBench: A Dynamically Evolving Real-World Labor-Market Agentic Benchmark Framework Built for Human-Centric AI](https://mlsanity.com/paper/2511.12306/) (2025)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [Trustworthy Human Computation: A Survey](https://mlsanity.com/paper/2210.12324/) (2022)
- [Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality](https://mlsanity.com/paper/2309.04022/) (2023)
- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)
- [Selective Progress-Aware Querying for Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2509.20541/) (2025)
- [Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement](https://mlsanity.com/paper/2504.17393/) (2025)
- [Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect](https://mlsanity.com/paper/2512.04764/) (2025)
- [Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters](https://mlsanity.com/paper/2607.17548/) (2026)

## Abstract (arXiv)

Mixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Additionally, many users desire the ability to have a greater level of control and fix perceived flaws in systems they rely on. However, how the ability to provide feedback to autonomous systems influences user trust is a largely unexplored area of research. Our research investigates how the act of providing feedback can affect user understanding of an intelligent system and its accuracy. We present a controlled experiment using a simulated object detection system with image data to study the effects of interactive feedback collection on user impressions. The results show that providing human-in-the-loop 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. These results highlight the importance of considering the effects of allowing end-user feedback on user trust when designing intelligent systems.