# Progressive Disclosure: Designing for Effective Transparency

arXiv: https://arxiv.org/abs/1811.02164 · HTML: https://mlsanity.com/paper/1811.02164/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces](https://mlsanity.com/paper/2510.22922/) (2025)
- [OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning](https://mlsanity.com/paper/2403.05565/) (2024)

## Abstract (arXiv)

As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to global and incremental feedback in two studies. In Study 1, users anticipated that the more transparent incremental system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this may arise because incremental feedback is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.