# The AI Index 2021 Annual Report

arXiv: https://arxiv.org/abs/2103.06312 · HTML: https://mlsanity.com/paper/2103.06312/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2021.

## Built on by (most cited first, 30 of 34)

- [Mapping global dynamics of benchmark creation and saturation in artificial intelligence](https://mlsanity.com/paper/2203.04592/) (2022)
- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Ecosystem Graphs: The Social Footprint of Foundation Models](https://mlsanity.com/paper/2303.15772/) (2023)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- [Why and How Governments Should Monitor AI Development](https://mlsanity.com/paper/2108.12427/) (2021)
- [Pre-registration for Predictive Modeling](https://mlsanity.com/paper/2311.18807/) (2023)
- [A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks](https://mlsanity.com/paper/2110.01434/) (2021)
- [JUWELS Booster -- A Supercomputer for Large-Scale AI Research](https://mlsanity.com/paper/2108.11976/) (2021)
- [A brief history of AI: how to prevent another winter (a critical review)](https://mlsanity.com/paper/2109.01517/) (2021)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [FairJob: A Real-World Dataset for Fairness in Online Systems](https://mlsanity.com/paper/2407.03059/) (2024)
- [Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities](https://mlsanity.com/paper/2111.02244/) (2021)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [The social dilemma in artificial intelligence development and why we have to solve it](https://mlsanity.com/paper/2107.12977/) (2021)
- [AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications](https://mlsanity.com/paper/2403.09097/) (2024)
- [Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment](https://mlsanity.com/paper/2406.11039/) (2024)
- [Accountability in AI: From Principles to Industry-specific Accreditation](https://mlsanity.com/paper/2110.09232/) (2021)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [Defining data science: a new field of inquiry](https://mlsanity.com/paper/2306.16177/) (2023)
- [Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers](https://mlsanity.com/paper/2307.10198/) (2023)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Measuring Ethics in AI with AI: A Methodology and Dataset Construction](https://mlsanity.com/paper/2107.11913/) (2021)
- [EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms](https://mlsanity.com/paper/2305.14169/) (2023)
- [Automated Essay Scoring in Argumentative Writing: DeBERTeachingAssistant](https://mlsanity.com/paper/2307.04276/) (2023)
- [Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach](https://mlsanity.com/paper/2403.19083/) (2024)
- [Developing Strategies to Increase Capacity in AI Education](https://mlsanity.com/paper/2509.21713/) (2025)
- [Machine Learning Featurizations for AI Hacking of Political Systems](https://mlsanity.com/paper/2110.09231/) (2021)
- [Machines & Influence: An Information Systems Lens](https://mlsanity.com/paper/2111.13365/) (2021)

## Abstract (arXiv)

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.