Library

Towards a Framework for Openness in Foundation Models

This paper presents a framework for grappling with openness across the AI stack. The paper surveys existing approaches to defining openness in AI models and systems, and then proposes a descriptive framework to understand how each component of the foundation model stack contributes to openness.

Mind the Gap: What Working With Developers on Fuzz Tests Taught Us About Coverage Gaps

AI Intersections Database

The AI Intersections Database maps intersections between the key social justice and human rights areas of our time and documented AI impacts and their manifestations in society.

Full Disclosure: Stress testing tech platforms’ ad repositories

Mozilla called on Check First to test the ad transparency tools maintained by 11 of the world's largest tech companies. These tools aim to let researchers, watchdogs, and members of the public understand how commercial communications influence the information space and affect society.

Technical Readout - Columbia Convening on Openness and AI

Key technical takeaways from a convening focused on exploring what "openness" means in the AI era.

Policy Readout - Columbia Convening on Openness and AI

This policy brief distills the discussions at Columbia Convening in 2024 for policymakers - identifying benefits that openness can bring to the AI ecosystem, the risk/benefit tradeoffs in opening up different components of the AI stack, and recommendations for policymakers.

AI Report on Southern Africa

As governments, communities, and businesses across southern Africa continue to integrate artificial intelligence technologies into everyday life, they face three key challenges: AI readiness, AI deployment, and potential misuse of AI.

Mozilla’s Elections Casebook

Mozilla's Elections Casebook scrutinizes what steps platforms have taken to protect election integrity globally. Case studies explore the impact of platform policies and interventions.

In Transparency We Trust?

Evaluating the Effectiveness of Watermarking and Labeling AI-Generated Content

Accelerating Progress Toward Trustworthy AI

Status update on our 2020 paper “Creating Trustworthy AI” and next steps to promote openness, competition, and accountability in AI