Tag Archive: AI safety

Repurposing the Wheel: Lessons for AI Standards

Mina Narayanan, Alexandra Seymour, Heather Frase, and Karson Elmgren
| November 2023

Standards enable good governance practices by establishing consistent measurement and norms for interoperability, but creating standards for AI is a challenging task. The Center for Security and Emerging Technology and the Center for a New American Security hosted a series of workshops in the fall of 2022 to examine standards development in the areas of finance, worker safety, cybersecurity, sustainable buildings, and medical devices in order to apply the lessons learned in these domains to AI. This workshop report summarizes our findings and recommendations.

On July 21, the White House announced voluntary commitments from seven AI firms to ensure safe, secure, and transparent AI. CSET’s research provides important context to this discussion.

Exploring Clusters of Research in Three Areas of AI Safety

Helen Toner and Ashwin Acharya
| February 2022

Problems of AI safety are the subject of increasing interest for engineers and policymakers alike. This brief uses the CSET Map of Science to investigate how research into three areas of AI safety — robustness, interpretability and reward learning — is progressing. It identifies eight research clusters that contain a significant amount of research relating to these three areas and describes trends and key papers for each of them.

Specifying Objectives is Key to AI Safety

EE Times
| December 1, 2021

A new CSET report by Tim Rudner and Helen Toner introduces specification in machine learning as a key element in greater AI safety.

Key Concepts in AI Safety: Specification in Machine Learning

Tim G. J. Rudner and Helen Toner
| December 2021

This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.

Algorithmic Warfare: How AI Could Go Disastrously Wrong

National Defense
| September 8, 2021

CSET's Director of Strategy Helen Toner sat down with National Defense to discuss AI failures from her and Zachary Arnold's CSET report "AI Accidents: An Emerging Threat."

AI in Automotive: Current and Future Impact

EE Times
| August 30, 2021

Looking at AI in the automotive industry, a CSET report "AI Accidents: An Emerging Threat" identifies AI failures and calls for a greater emphasis on research and development to improve safety.

Senior Fellow Andrew Lohn discusses the threats and vulnerabilities AI systems, especially the Pentagon's, are susceptible to in his recent CSET report Poison in the Well

As the use of AI technology becomes more common, more problems arise prompting the need for policy and government responses, according to CSET's Helen Toner.