Talk to the Institute

Alpha Institute • Responsible AI Observatory

Research that makes governance examinable.

Read foundational work on documentation, evaluation, accountability and systemic risk.

Talk to Alpha

Use research with context

Retain author, publication venue, version, source and retrieval provenance. Separate a paper’s findings from Alpha interpretation and from claims about a particular organization.

Independent editorial review

Research candidates require source and rights review, reviewer attribution, conflict disclosure and revision history before publication as Alpha conclusions. Sponsorship or Council membership does not control findings.

Foundational research library

Reading candidates, not approved methodology, verified legal interpretations or independent findings. Publication dates and versions marked unknown require review. No control mappings have been approved in this collection.

13 resources

Model Cards for Model Reporting

Documents intended uses, evaluation conditions and limitations of trained models.

Margaret Mitchell et al. • Model documentation • Research

Version: v2 • Publication: Unknown

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Datasheets for Datasets

Proposes structured documentation of dataset motivation, composition, collection and intended use.

Timnit Gebru et al. • Data documentation • Research

Version: Unknown • Publication: Unknown

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Closing the AI Accountability Gap

Presents an end-to-end framework for internal algorithmic auditing.

Inioluwa Deborah Raji et al. • Accountability • Research

Version: v1 • Publication: 2020-01-03

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

On the Dangers of Stochastic Parrots

Examines environmental, financial and societal concerns associated with large language models.

Emily M. Bender et al. • Societal risk • Research

Version: FAccT 2021 • Publication: 2021-03-01

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Holistic Evaluation of Language Models

Examines language models across multiple scenarios and metrics; the linked revision is dated 2023-10-01.

Percy Liang et al. • Evaluation • Research

Version: v2 • Publication: 2022-11-16

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

The AI Risk Repository

A research entry point to risk classifications and the associated meta-review.

Peter Slattery et al. • Risk taxonomy • Research

Version: Unknown • Publication: Unknown

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

The Foundation Model Transparency Index

The foundational 2023 paper. Later editions exist; do not treat historical findings as current developer assessments.

Rishi Bommasani et al. • Transparency • Research

Version: v1 • Publication: 2023-10-19

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Responsible Practices for Synthetic Media

A framework for responsible development, creation and sharing of synthetic media, organized around consent, disclosure and transparency.

Partnership on AI • Synthetic media • Research

Version: Unknown • Publication: 2023-02-22

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Measuring AI Ability to Complete Long Software Tasks

Research on measuring agent capability through task duration and reliability. Its benchmark results do not establish safety or performance in a particular enterprise deployment.

METR • Agent evaluation • Research

Version: Unknown • Publication: 2025-03-19

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Project Moonshot

Primary project documentation for an open-source testing toolkit covering LLM application benchmarks and red teaming. Tool availability is not an assurance conclusion.

AI Verify Foundation • Evaluation toolkit • Research

Version: Unknown • Publication: Unknown

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Algorithmic accountability for the public sector

Research on early algorithmic accountability policies in the public sector. Scope and historical context matter when applying its findings.

Ada Lovelace Institute, AI Now Institute and Open Government Partnership • Accountability • Research

Version: Unknown • Publication: 2021-08-24

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Artificial Power: 2025 Landscape Report

An institutional research report examining power and public agency in the AI landscape. The report’s analysis remains attributable to its authors.

AI Now Institute • Societal risk • Research

Version: Unknown • Publication: 2025-06-03

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Responsible AI Institute research and governance resources

Discovery entry for the Institute’s public research and governance resources. Member-only material is excluded; this entry is not a specific publication or an Alpha certification claim.

Responsible AI Institute • Institutional source directory • Research

Version: Unknown • Publication: Unknown

Review: pending • Last reviewed: not yet reviewed • Check source for revisions

Citation and original summary only; no full-text ingestion

Machine-readable resources

Alpha Evals

Explore existing readiness and independent evaluation pathways. https://evals.alpha.ac/

Explore

Choose the relevant path.

Responsible AI Observatory

Standards, research and board education connecting Responsible AI principles to accountable governance.

Explore

Standards inform governance. Scope determines applicability.

An Observatory reading guide to primary sources, with clear separation between law, standards and recommendations.

Explore

Make risk accountable.

Connect observed events, plausible risks and control decisions without conflating them.

Explore

Build judgment before the consequential decision.

Draft learning guides for directors, committees and executives. These materials are educational and await editorial approval.

Explore

Make the method visible.

Versioned scope, evidence and review are prerequisites for defensible evaluation.

Explore

Comparable evidence comes before comparison.

No Responsible AI benchmark population has been approved for publication here.

Explore

Evidence supports a conclusion only within its stated scope.