Center for AI Safety
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About
The Center for AI Safety (CAIS) is a San Francisco-based non-profit research and field-building organization dedicated to reducing societal-scale risks associated with artificial intelligence. Recognizing that AI has the potential for profound benefit as well as catastrophic harm, the organization focuses on the technical and conceptual challenges of ensuring AI systems are developed and deployed safely. It functions as a bridge between academic research, public policy, and private industry, addressing problems that have yet to be solved despite rapid progress in AI capabilities. The center operates through a multidisciplinary approach, conducting both technical and conceptual research. Its technical laboratory creates foundational benchmarks and methods used by the scientific community to test and improve AI safety, with all datasets and code released publicly. Conceptually, CAIS integrates insights from safety engineering, international relations, and philosophy to create frameworks for understanding future societal risks. They also support the ecosystem through a compute cluster for researchers, a philosophy fellowship, and regular newsletters like the AI Safety Newsletter and MLS Newsletter. CAIS is primarily for machine learning researchers, safety engineers, policymakers, and academics who are concerned with long-term AI alignment and risk mitigation. It provides essential resources for those looking to enter the field or stay updated on the latest safety protocols and technical benchmarks. By providing accessible data and publishing at top ML conferences, it supports the broader scientific community in standardizing safety practices. Unlike for-profit AI labs focused on commercialization, CAIS is a non-profit that prioritizes transparency and public goods. Its unique value lies in its multidisciplinary lens—combining technical rigor with social science and philosophy—to address risks that are often overlooked in standard product development. Their commitment to releasing all work publicly ensures that safety advancements are shared globally rather than kept as proprietary secrets.
Pros & Cons
Releases all datasets and code publicly for global transparency
Multidisciplinary approach involving philosophy and international relations
Focuses specifically on societal-scale and catastrophic risks
Provides resources like compute clusters and fellowships to build the field
Publishes research in top-tier machine learning conferences
Focuses on high-level societal risks rather than immediate software bugs
Compute cluster access is primarily restricted to safety researchers
Non-profit model relies heavily on external donations and grants
Use Cases
Machine learning researchers can use foundational benchmarks and open-source datasets to test the safety and alignment of their AI models.
Policymakers can leverage CAIS frameworks and conceptual research to develop informed safety standards and regulations for AI deployment.
Graduate students can apply for the philosophy fellowship or field-building projects to transition into the specialized field of AI safety.
Journalists and media members can access the AI Safety Newsletter to stay informed about the latest developments and risks in the AI landscape.
Academic institutions can reference CAIS publications at top ML conferences to integrate safety-first methodologies into their computer science curricula.
Platform
Features
• public policy advocacy
• philosophy fellowship
• ml research compute cluster
• ai safety newsletter
• open-source code releases
• conceptual safety frameworks
• foundational safety benchmarks
• technical safety research
FAQs
What is the primary mission of the Center for AI Safety?
The mission is to reduce societal-scale and potentially catastrophic risks associated with artificial intelligence. This is achieved through technical research, field-building among researchers, and advocating for global safety standards.
Does CAIS provide technical tools for researchers?
Yes, CAIS creates foundational benchmarks and methods that the scientific community can use to address technical safety challenges. They release all datasets and code publicly to ensure transparency and accessibility for all ML researchers.
How can researchers get involved with CAIS?
Researchers can engage with CAIS through their field-building projects, apply for the philosophy fellowship, or utilize their compute cluster for safety work. They also provide regular updates via the AI Safety Newsletter and the MLS Newsletter.
Is CAIS research available to the public?
Absolutely, all CAIS research is public and accessible. They publish their findings in top machine learning conferences and ensure that all datasets and code are released to the broader community.
Where is the organization based and how is it funded?
CAIS is a 501(c)(3) non-profit organization based in San Francisco, California. It is supported by donations, which are tax-exempt in the United States, to maintain its independent research and advocacy efforts.
Pricing Plans
Open Access
Free Plan• Public research publications
• Open-source safety benchmarks
• Accessible datasets and code
• AI Safety Newsletter
• Field-building projects
• Educational resources
• Public advocacy materials
Job Opportunities
Operations Associate
Mitigate catastrophic risks from advanced AI through technical research, foundational benchmarks, and safety advocacy for the global machine learning community.
Benefits:
Health insurance for you and your dependents
401K plan + 4% matching
Unlimited PTO
Lunch and dinner at the office
Annual Professional Development Stipend
Education Requirements:
Bachelor’s degree
Experience Requirements:
1-3 years of relevant experience (operations/office management/program management, ideally in a fast-moving environment)
Other Requirements:
Strong organizational and prioritization skills
Strong judgment + critical thinking
Strong communication skills
Ability to learn and operate within new systems quickly (Google Workspace, Slack, Administering Tools)
High attention to detail
Responsibilities:
Own day-to-day office operations to keep the workspace functional, stocked, and welcoming
Serve as the primary point of contact with building management for facilities requests
Triage and fulfill access requests for approved tools, provision accounts and maintain clean tracking
Coordinate onboarding and offboarding logistics and serve as the primary point of contact for new joiners
Provide administrative support to Operations leadership
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Program Manager
Mitigate catastrophic risks from advanced AI through technical research, foundational benchmarks, and safety advocacy for the global machine learning community.
Benefits:
Health insurance for you and your dependents
401K plan + 4% matching
Unlimited PTO
Lunch and dinner at the office
Annual Professional Development Stipend
Experience Requirements:
3+ years of experience
Other Requirements:
Strong understanding of project management techniques
Motivated by work that is focused on AI safety and risk
Conscientious and produce high quality work
Adaptive, able to rapidly transfer to new domains while staying organized
Open to new ideas and feedback, honest with yourself and others
Responsibilities:
Conduct preliminary research and exploration for new projects to establish clear goals, scope, and requirements
Collaborate with the Program Director (PD) to define project plans including objectives, deliverables, timelines, and resources
Take ownership of project implementation and ensure the project is delivered in line with expectations
Identify, assess, and mitigate risks to ensure successful project execution
Manage relationships with external partners, vendors or other stakeholders to ensure project requirements are met
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