The Normalization of Deviance in AI Development

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

arXiv:2607. 14353v1 Announce Type: cross Abstract: As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems.

Position: AI Lock-In Is in Progress, and We Must Be Prepared

arXiv:2608. 14565v1 Announce Type: new Abstract: AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of generative AI's societal impacts (including unemployment risk and labor market disruption).

Why responsible AI development needs cooperation on safety

We’ve written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety.

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.

Democratizing AI Safety with RiskRubric.ai

Our approach to AI safety

Ensuring that AI systems are built, deployed, and used safely is critical to our mission.