# Measurement in AI Policy: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2009.09071 · HTML: https://mlsanity.com/paper/2009.09071/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

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

- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials](https://mlsanity.com/paper/2105.06370/) (2021)
- [Measuring Ethics in AI with AI: A Methodology and Dataset Construction](https://mlsanity.com/paper/2107.11913/) (2021)
- [Principles and Guidelines for Randomized Controlled Trials in AI Evaluation](https://mlsanity.com/paper/2605.02050/) (2026)

## Abstract (arXiv)

As artificial intelligence increasingly influences our world, it becomes crucial to assess its technical progress and societal impact. This paper surveys problems and opportunities in the measurement of AI systems and their impact, based on a workshop held at Stanford University in the fall of 2019. We identify six summary challenges inherent to measuring the progress and impact of AI, and summarize over 40 presentations and associated discussions from the workshop. We hope this can inspire research agendas in this crucial area.