# The Balancing Act of Policies in Developing Machine Learning Explanations

arXiv: https://arxiv.org/abs/2504.13946 · HTML: https://mlsanity.com/paper/2504.13946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)

## Abstract (arXiv)

Machine learning models are often criticized as opaque from a lack of transparency in their decision-making process. This study examines how policy design impacts the quality of explanations in ML models. We conducted a classroom experiment with 124 participants and analyzed the effects of policy length and purpose on developer compliance with policy requirements. Our results indicate that while policy length affects engagement with some requirements, policy purpose has no effect, and explanation quality is generally poor. These findings highlight the challenge of effective policy development and the importance of addressing diverse stakeholder perspectives within explanations.