Research & innovation
Our proposed research direction connects careful evaluation with responsible use. Start with the questions, then inspect the evidence and its limits.
A proposed direction
These themes describe collaboration interests. They do not represent completed projects or published findings.
Proposed theme
How can we understand where an AI system works, where it fails, and how reliably it behaves when conditions change?
We propose investigating evaluation methods that connect benchmark results with the demands of real tasks. This includes testing failure modes, documenting uncertainty, and making the limits of evidence clear before deployment decisions.
Connects research · practical solutions · governance
Explore a collaborationProposed theme
What evidence, safeguards, and human decisions are needed before and after an AI system enters practical use?
We propose studying how evaluation, privacy, accountability, and human oversight can inform deployment decisions. The focus is on defining useful boundaries, escalation paths, and ways to learn from real-world feedback.
Connects research · practical solutions · governance
Explore a collaborationZero One Labs outputs
Approved, published work will appear here with its authors, methods, evidence, and limitations.
Explore our proposed research direction and collaboration interests.
Discuss researchFrom the wider research community
Selected resources from other organizations, with our short editorial summaries. These are not Zero One Labs publications, endorsements, or partnerships.
3 results
A voluntary framework for considering AI risks and trustworthiness throughout the design, development, deployment, and use of AI systems.
A proposal for documenting a dataset's motivation, composition, collection process, and intended uses to improve transparency and communication.
An approach to evaluating language models across multiple scenarios and metrics, making the coverage and limits of evaluation explicit.
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Research principles
Explain what is being investigated, for whom, and under which conditions an answer could be useful.
Document methods, uncertainty, limitations, and sources so readers can assess what an output supports.
Connect findings to human oversight, privacy, and accountability before proposing practical use.