# Towards evaluating and eliciting high-quality documentation for intelligent systems

arXiv: https://arxiv.org/abs/2011.08774 · HTML: https://mlsanity.com/paper/2011.08774/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://mlsanity.com/paper/2409.19801/) (2024)
- [Evaluating a Methodology for Increasing AI Transparency: A Case Study](https://mlsanity.com/paper/2201.13224/) (2022)
- [Promoting Critical Thinking With Domain-Specific Generative AI Provocations](https://mlsanity.com/paper/2603.19975/) (2026)
- [Automatic Model Card Generation Using an LLM](https://mlsanity.com/paper/2608.24807/) (2026)

## Abstract (arXiv)

A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation. However, such systems are notorious for their complexity and opaqueness making quality documentation a non-trivial task. Furthermore, little is known about what makes such documentation "good." In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short. Then, using those dimensions, we evaluate three different approaches for eliciting intelligent system documentation. We show how the dimensions identify shortcomings in such documentation and posit how such dimensions can be use to further enable users to provide documentation that is suitable to a given persona or use case.