Theory of Change

System Overview

HΩ is a collaboration platform that accelerates AI safety research by facilitating high-impact projects and connecting experts, researchers, and professionals.

Key components:

• Project Management: Proposal submission, review, tracking, and collaboration tools
• Matchmaking: AI-powered recommendations for project-member connections based on expertise and interests
• Funding Facilitation: Support for external grant applications and resources
• Impact Evaluation: Metrics, data analysis, and reporting tools to assess project quality, relevance, and impact

Core Flows

Projects

• Submission and Review: Well-defined proposals assessed for impact, feasibility, and alignment with AI safety goals
• Execution and Evaluation: Teams collaborate, track progress, and assess impact using predefined criteria
• Reporting: Insights, outcomes, and challenges shared with the community to foster transparency and knowledge-sharing

Members

• Discovery and Contribution: Tailored opportunities and efficient collaboration through AI-powered matchmaking
• Development and Leadership: Workshops, mentorship, and community guidance to support professional growth and project success
• Value Proposition: Access to funding, collaboration opportunities, and resources to advance AI safety research

Theory of Change

HΩ accelerates AI safety research by:

1. Increasing the number of active AI safety projects on the platform

   • Measured by the number of projects that meet predefined criteria for activity and progress
   • Achieved through targeted outreach, community engagement, and support for project teams

2. Facilitating successful grant applications and partnerships for AI safety projects

   • Measured by the number of projects that secure external funding or resources through HΩ's support
   • Achieved by providing proposal guidance, connecting projects with potential partners and funders, and showcasing project impact

3. Engaging new members with relevant expertise in AI safety

   • Measured by the number of new members who meet predefined criteria for expertise and engagement
   • Achieved through targeted outreach, partnerships, and the value proposition for members

4. Producing high-quality research outputs and practical tools

   • Measured by the number of outputs that meet predefined criteria for quality, relevance, and impact
   • Achieved by supporting and showcasing projects that aim for progress in AI safety R&D

HΩ aims to contribute to the overall progress and advancement of AI safety research, ultimately supporting the development of safe and beneficial AI systems.