A USAISI Workshop: Collaboration 

to Enable Safe and Trustworthy AI

1
US AI Safety Institute Consortium

Elham Tabassi 

Chief AI Advisor

elham.tabassi@nist.gov
3

March 2023

Trustworthy AI 

Resource Center

June 2023

Generative AI Public 

Working Group

October 2023

EO 14110

Jan 2023

AI RMF 1.0

Major achievements and announcements in 2023 

3

November 2023

US AI Safety Institute 

Announced
Roadmap of future activities released in January 

2023.
Building and maturing a measurement science for 

trustworthy and responsible AI.

Conduct 

evaluations

Develop guidelines 

and standards

Create test 

environments

Build and expand 

the science of AI 

evaluation 
For more information, we encourage you

to access NIST resources,

or reach out directly!

www.nist.gov/artificial-intelligence/artificial-intelligencesafety-institute

airc.nist.gov

usaisi@nist.gov
US AI Safety Institute Consortium

Jake Taylor

Senior Advisor for Critical and Emerging Technologies

jacob.taylor@nist.gov
US AI Safety Institute: Three Pillars

GOAL: Drive work and oversee 

research collaboration with 

partners

Example activities

• Working groups

• Scientific collaborations

• Shared guidance

• Draft standards

• Aligned approaches

Consortium

GOAL: Guide implementation 

of scientific findings, tests, and 

risk management frameworks

Example activities

• Metrics and methodologies

• Development of testbeds

• Evaluations and redteaming

• Use-case-specific risk 

management “profiles”

Implementation

GOAL: Improve the science of 

AI safety

Example activities

• Fundamental research

• Technical building blocks

• Content authentication 

guidance and best 

practices

Research
NIST Consortia: Public-Private Partnerships to 

Address Pre-competitive Challenges

NIST GENOME IN A BOTTLE 

(GIAB) CONSORTIUM 

NIST GENOME EDITING 

CONSORTIUM

NIST FLOW CYTOMETRY 

STANDARDS CONSORTIUM

NIST RAPID MICROBIAL 

TESTING METHODS 

CONSORTIUM

Accelerates the 

adoption of 

quantitative flow 

cytometry in 

biomanufacturing

Develops measurement 

solutions and standards 

needed to increase 

confidence and 

reduce risk

Provides authoritative 

characterization of 

benchmark human 

genomes

Addresses measurements 

and standards needed 

to increase confidence 

in the use of rapid testing
Global Documentary Standards Leadership:

Example from Biotechnology

NIST 

Laboratory 

Programs

Working with 

Stakeholders

Global 

Standards 

Leadership

Industry, USG, NGOs, 

etc.

NIST Reference 

Materials:

Calibration, traceability, 

comparability 

• Advanced 

metrology

• DBTL

• Standards

NIST Consortia 

NIST-led Standards
Why a consortium?

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• Enable a shared space for complex conversations and 

research collaboration

• Where possible, take an open and transparent approach

• Bring together actors from public and private sector and 

strategic international partners

• Interested parties can work together in building and 

maturing a new measurement science for trustworthy AI

• Work together 

• to align capability evaluation and red-teaming guidance; 

• to enable AI testbeds and test environments; 

• to build the foundations and documentation for standards
What is the approach?

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Illustrative example: 

https://www.nccoe.nist.gov/publications/other/nccoe-consortium-crada-example

NIST-led consortia built from a cooperative research and 

development agreement – everyone signs the same one

Initial activities guided through working 

groups – discussion after the break!

Send in your letter of interest!

https://www.nist.gov/artificial-intelligence/artificial-intelligence-safety-institute

Driven from experiences with the Generative AI Public Working Group
What can 

we do 

together?

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Develop guidance and benchmarks for identifying and evaluating AI Guidance capabilities, with a focus on capabilities that could potentially cause harm

Develop approaches to incorporate secure-development practices for 

generative AI, including special considerations for dual-use foundation 

models

Security

Testing Develop and ensure the availability of testing environments

Develop guidance, methods, skills and practices for successful red-teaming 

and privacy-preserving machine learning Red-teaming

Tools Develop guidance and tools for authenticating digital content

Develop guidance and criteria for AI workforce skills, including risk 

identification and management, test, evaluation, validation, and 

verification (TEVV), and domain-specific expertise

Workforce

Explore the complexities at the intersection of society and technology, 

including the science of how humans make sense of and engage with AI in 

different contexts

Society

Develop guidance for understanding and managing the 

interdependencies between and among AI actors along the lifecycle Lifecycle
US AI Safety Institute Consortium

Kamie Roberts, NIST AI Executive Order Program Manager 

(kathleen.roberts@nist.gov)

Reva Schwartz, Research Scientist, Principal Investigator for AI Bias, 

(reva.schwartz@nist.gov)
Foundation for Success

Build the foundation for sustained and continuous efforts 

to create safe and trustworthy AI

● Advance research

● Facilitate consensus standards

● Create test environments 

● Enable evaluations to assess the risk and impact of 

current and next generation AI on individuals and society

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Working Groups: Role and Outputs

Support development of Safe and Trustworthy AI through 

● Convening space for dialogue and information sharing

● Collaborative R&D

● Evaluations of test systems and prototypes

Outputs

● New guidelines, tools, methods, protocols and best practices

● Guidance and benchmarks for AI capabilities - particularly 

those that may cause harm

● Availability of testing environments 16
Proposed Working Groups

Evaluating AI 

Capabilities

Synthetic 

Content Generative AI

Society and

Technology
Proposed Working Groups

● Generative AI

○ Risks and capabilities

● Synthetic content

○ Authentication, detection, labeling 

○ Deep fakes

● Evaluating AI capabilities

○ AI red-teaming and other testing methodologies

○ Pre-deployment testing and post-deployment monitoring

● Society and Technology

○ Standards 

○ Operationalize the AI RMF 18