The rapid advancement of AI by private labs has created a transparency gap, making it hard for outsiders to assess their decisions, ethical alignment, or societal impact. Without independent oversight, society risks ceding too much influence to unaccountable entities shaping technologies with far-reaching consequences.

How It Could Work

One approach could be a platform that systematically tracks and critiques major AI labs' actions, such as:

• Technical choices (model designs, safety protocols)
• Ethical implications (bias risks, dual-use potential)
• Transparency practices (public disclosures, stakeholder engagement)

Public data—research papers, announcements, leaks—could be aggregated and presented through digestible formats like scorecards or timelines. Over time, crowdsourced critiques or expert panels might diversify perspectives while maintaining rigor.

Who Could Benefit

This could serve:

• Researchers needing centralized, unbiased analyses
• Policymakers lacking technical capacity for oversight
• Journalists seeking accurate, simplified explanations
• The public wanting to understand AI's societal impact

Execution Pathways

A phased approach might start with a newsletter dissecting lab announcements, then evolve into a tagged database of actions (e.g., tracking disclosure frequency). Later stages could introduce interactive tools like lab "report cards" or community annotation features. Early tests could gauge demand through pilot sign-ups or by compiling sample timelines of lab activities.

Key challenges—like labs withholding information—might be addressed by focusing on public disclosures initially, while partnerships with ethicists and diverse funding could help maintain independence and nuance.