Jack Clark

Co-Founder - Anthropic

Working groupExpert Group on Compute & Climate

Working groupExpert Group on AI risk and accountability

Stakeholder TypeTechnical community

GPAI

ONE AI Member

AI Wonk contributor

Jack Clark is the Policy Director for OpenAI, an AI research organisation ensuring that the benefits of artificial general intelligence are widely and evenly distributed. Jack predominantly works on policy and safety issues. He also helps develop the AI Index, an AI forecasting and progress initiative that is part of the Stanford One Hundred Year Study on AI. In his spare time, he writes an AI newsletter, Import AI (importai.net) which is read by more than ten thousand experts. Twitter: @jackclarksf.

Jack Clark's publications

Intergovernmental

The OECD Framework for Classifying AI Systems to assess policy challenges and ensure international standards in AI

The classification framework can help policy makers understand and map the affects of AI systems on societies and economies.

February 17, 2022 — 4 min read

Academia

The 2021 AI Index: AI Industrialization and Future Challenges

In 2020, a new class of large-scale AI models emerged in the research community and one fourth of countries have an AI strategy.

June 9, 2021 — 4 min read

Intergovernmental

Measuring compute capacity: a critical step to capturing AI’s full economic potential

To date, many governments do not know how much compute capacity they need to achieve their AI goals.

February 8, 2022 — 6 min read

Intergovernmental

A first look at the OECD’s Framework for the Classification of AI Systems, designed to give policymakers clarity

Find out about the OECD’s Framework for the Classification of AI Systems and follow the next steps.

November 24, 2020 — 5 min read

Jack Clark's videos

The OECD Al Systems Classification Framework

February 6, 2021–

90 mins

The OECD’s Network of Experts on AI developed a user-friendly framework to classify AI systems. It provides a structure for assessing and classifying AI systems according to their impact on public policy following the OECD AI Principles. This session discusses the four dimensions of the draft OECD AI Systems Classification Framework, illustrates the usefulness of the framework using concrete AI systems as examples, and seeks feedback and comments to support finalisation of the framework. Aclassification framework to understand the labour market impact will also be introduced.

The OECD Framework for the Classification of AI Systems

February 2, 2021–

4 mins

Different types of AI systems raise very different policy opportunities and challenges. As part of the AI-WIPS project, the OECD has developed a user-friendly framework to classify AI systems. The framework provides a structure for assessing and classifying AI systems according to their impact on public policy in areas covered by the OECD AI Principles.

Jack Clark's documents

AI Index Report 2021 Chapter 7

This chapter navigates the landscape of AI policymaking and tracks efforts taking place on the local, national, and international levels to help promote and govern AI technologies. It begins with an overview of national and regional AI strategies and then reviews activities on the intergovernmental level. The chapter then takes a closer look at public investment in AI in the United States as well as how legislative bodies, central banks, and nongovernmental organizations are responding to the growing need to institute a policy framework for AI technologies.— March 15, 2021

AI Index Report 2021 Chapter 6

This chapter presents diversity statistics within the AI workforce and academia. It draws on collaborations with various organizations—in particular, Women in Machine Learning (WiML), Black in AI (BAI), and Queer in AI (QAI)— each of which aims to improve diversity in some dimension in the field. The data is neither comprehensive nor conclusive. In preparing this chapter, the AI Index team encountered significant challenges as a result of the sparsity of publicly available demographic data. The lack of publicly available demographic data limits the degree to which statistical analyses can assess the impact of the lack of diversity in the AI workforce on society as well as broader technology development. The diversity issue in AI is well known, and making more data available from both academia and industry is essential to measuring the scale of the problem and addressing it.— March 15, 2021

AI Index Report 2021 Chapter 5

This chapter tackles the efforts to address the ethical issues that have arisen alongside the rise of AI applications. It first looks at the recent proliferation of documents charting AI principles and frameworks, as well as how the media covers AI-related ethical issues. It then follows with a review of ethics-related research presented at AI conferences and what kind of ethics courses are being offered by computer science (CS) departments at universities around the world.— March 15, 2021

AI Index Report 2021 Chapter 4

This chapter focuses on trends in the skills and training of AI talent through various education platforms and institutions. What follows is an examination of data from an AI Index survey on the state of AI education in higher education institutions, along with a discussion on computer science (CS) undergraduate graduates and PhD graduates who specialized in AI-related disciplines, based on the annual Computing Research Association (CRA) Taulbee Survey. The final section explores trends in AI education in Europe, drawing on statistics from the Joint Research Centre (JRC) at the European Commission.— March 15, 2021

AI Index Report 2021 Chapter 3

This chapter looks at the increasingly intertwined relationship between AI and the global economy from the perspective of jobs, investment, and corporate activity. It first analyzes the worldwide demand for AI talent using data on hiring rates and skill penetration rates from LinkedIn as well as AI job postings from Burning Glass Technologies. It then looks at trends in private AI investment using statistics from S&P Capital IQ (CapIQ), Crunchbase, and Quid. The third, final section analyzes trends in the adoption of AI capabilities across companies, trends in robot installations across countries, and mentions of AI in corporate earnings, drawing from McKinsey’s Global Survey on AI, the International Federation of Robotics (IFR), and Prattle, respectively.— March 15, 2021

AI Index Report 2021 Chapter 2

This chapter highlights the technical progress in various subfields of AI, including computer vision, language, speech, concept learning, and theorem proving. It uses a combination of quantitative measurements, such as common benchmarks and prize challenges, and qualitative insights from academic papers to showcase the developments in state-of-the-art AI technologies.— March 15, 2021

Academia

AI Index Report 2021 Chapter 1

This chapter begins by examining AI publications—from peer-reviewed journal articles to conference papers and patents, including the citation impact of each, using data from the Elsevier/Scopus and Microsoft Academic Graph (MAG) databases, as well as data from the arXiv paper preprint repository and Nesta. It examines contributions to AI R&D from major AI entities and geographic regions and considers how those contributions are shaping the field. The second and third sections discuss R&D activities at major AI conferences and on GitHub.— March 15, 2021

AI Index Report 2021

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 world’s most credible and authoritative source for data and insights about AI. 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 Stanford’s Institute for Human-Centered Artificial Intelligence (HAI).— March 15, 2021

Academia

AI Index Report 2019 – Introduction

The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. — July 8, 2020

The OECD AI Systems Classification Framework: progress, challenges and way forward

February 12, 2021

Academia

AI Index Report 2019 – Chapter 7 Public Perception

This chapter presents trends in public awareness of AI by tracking the AI mentions in public domains such as communications of central banks, parliament sessions in select countries and earning calls of publicly-traded companies in US.— July 7, 2020

Academia

AI Index Report 2019 – Chapter 6 Autonomous Systems

AI is a key component of Autonomous Systems. This chapter presents data on Autonomous Systems divided in two sections: Autonomous Vehicles (AV’s) and Autonomous Weapons (AW’s). The AV section shows the countries and cities testing AV’s. This is followed by US state policy on AV from the National Conference on State Legislation (NCSL). The section on AW presents the known types of autonomous weapon deployments and by which country based on expert survey data collected by the Stockholm International Peace Research Institute (SIPRI).— July 7, 2020

Academia

AI Index Report 2019 – Chapter 5 Education

This chapter presents trends in AI education from a variety of data sources, starting first with global data from Coursera and Udacity ML and AI training courses. Second, trends in undergraduate enrollment in introductory ML and AI courses are presented for the US and international universities. Third, trends in PhD hires on industry hiring, faculty hiring and faculty departures are presented based on the Taulbee Survey and Goffman and Jin (2019). Fourth, trends in gender and international diversity for AI PhDs are presented, along with faculty diversity across select university departments. — July 7, 2020

Academia

AI Index Report 2019 – Chapter 4 The Economy

This chapter is broken into three sections: Jobs, Investment Activity, and Corporate Activity. The first section on AI Jobs shows data relating to AI jobs, hiring, and skill levels around the globe as well as in US regions. The section concludes with trends in skill penetration and labor demand for AI jobs from a sub-regional US perspective. The second section on Investment presents startup investment trends for the world, by countries, and by sectors. The third section on Corporate Activity includes data on adoption of AI capabilities in industry, drawing from McKinsey’s Global AI survey. — July 7, 2020

Academia

AI Index Report 2019 – Chapter 8 Societal Considerations

This chapter discusses AI ethical challenges; key topics in global news on AI and Ethics; and AI use cases supporting each the UN SDGs.— June 30, 2020

Disclaimer: The opinions expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the official views of the OECD or its member countries. The Organisation cannot be held responsible for possible violations of copyright resulting from the posting of any written material on this website/blog.