# Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

arXiv: https://arxiv.org/abs/2006.16353 · HTML: https://mlsanity.com/paper/2006.16353/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

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

- [Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition](https://mlsanity.com/paper/2501.10909/) (2025)
- [Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI](https://mlsanity.com/paper/2511.00230/) (2025)
- [GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback](https://mlsanity.com/paper/2104.06600/) (2021)
- [Challenges and Trends in User Trust Discourse in AI](https://mlsanity.com/paper/2305.11876/) (2023)

## Abstract (arXiv)

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.