Meta’s Open Loop program is excited to have launched its first policy prototyping program in the United Kingdom, which is focused on testing the Competition and Markets Authority (CMA) AI Principles to ensure that they are clear, implementable and effective at guiding the ongoing development and use of AI Foundation Models, while protecting competition and consumers.

Meta’s Open Loop program is excited to be launching its first policy prototyping program in the United States, which is focussed on the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF) 1.0. The program will give consortium participants the opportunity to explore how the NIST AI RMF could help manage risks while developing and deploying Generative AI systems. At the same time, the program will seek to provide valuable insights and feedback to NIST as they work on future iterations of the RMF.

The EU AI Act program is the largest policy prototyping initiative to date, engaging over 60 participants from more than 50 companies developing AI and ML products. The program was structured into three pillars, each focusing on key articles of the EU proposal and assessing and scrutinizing them.

The Open Loop Brazil program was launched in tandem with a twin policy prototyping program in Uruguay, with the aim of guiding and enabling companies in Brazil to leverage and apply privacy-enhancing technologies (PETs) to help deidentify data and mitigate privacy-related risks.

The Open Loop India program was a collaborative effort between Meta, ArtEZ University of the Arts and The Dialogue, to develop a stakeholder engagement framework that operationalizes the principle of human-centered AI.

This program aimed to develop and test a risk assessment framework, called ADIA (Automated Decision Impact Assessment), for AI applications deployed in Europe.

The Open Loop Uruguay program was launched in tandem with a twin policy prototyping program in Brazil, with the aim of guiding and enabling companies in Uruguay to leverage and select privacy-enhancing technologies (PETs) to help de-identify data and mitigate privacy-related risks.