Measurement in AI Policy: Opportunities and Challenges
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributionscited by 5
- 2021Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentialscited by 3
- 2021Measuring Ethics in AI with AI: A Methodology and Dataset Constructioncited by 1
- 2026Principles and Guidelines for Randomized Controlled Trials in AI Evaluationcited by 0
- 2026Principles and Guidelines for Randomized Controlled Trials in AI Evaluation3 May 2026
- 2021Measuring Ethics in AI with AI: A Methodology and Dataset Construction26 Jul 2021
- 2021Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials13 May 2021
- 2021A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions5 Mar 2021