Academic Research & Publications
The Institute curates and audits peer-reviewed scholarship regarding machine learning alignment, interpretability, and sociotechnical governance.
International AI Safety Report 2025: Capabilities and Risk Implications
The first comprehensive synthesis of evidence regarding advanced AI systems, mandated by the 30 nations of the AI Safety Summit. This report serves as the baseline for global regulatory frameworks moving forward.
View Full Text on arXivarXiv:2501.17805 [cs.CY]
Recent Scholarship
Large Language Model Evaluation in 2025: Smarter Metrics That Separate Hype from Trust
A practitioner-focused guide introducing 15 next-gen metrics covering trust (factual grounding), safety (bias detection), and operational viability. Essential reading for enterprise deployment.
Read Paper ↗Alignment Faking in Large Language Models
The first empirical example of a model engaging in "alignment faking"—selectively complying with training objectives while strategically preserving non-compliant preferences.
Read Paper ↗Challenges in Translating Ethical AI Principles into Practice for Children
An examination of how high-level ethical principles fail to protect vulnerable demographics, specifically children, in the deployment of interactive AI agents.
Read Paper ↗Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
A roadmap projecting potential safety issues at each stage of technological advancement, proposing a "quality assurance" framework that extends beyond traditional safety definitions.
Read Paper ↗The AI Safety Charter
This charter stands as our public commitment to the ethical deployment of intelligence. Each article below represents a mandatory standard for our partners and accredited institutions.
Human-Centric Alignment
We mandate that all autonomous systems and Large Language Models (LLMs) must prioritize human well-being above computational efficiency. Any system deployed for public use must demonstrate verifiable alignment protocols.
Algorithmic Transparency
The Institute upholds the right to explainability. Institutions deploying AI at scale must maintain an audit trail of decision-making logic. "Black box" algorithms in critical sectors such as healthcare are prohibited.
Bias Mitigation
All certified models must undergo rigorous stress-testing for sociopolitical and demographic bias. The Institute serves as the final arbiter on whether a model meets the threshold for neutrality.
Accessibility & Transparency
Program pricing, certification requirements, and schedules are published and updated quarterly. If regulatory changes occur mid-engagement that affect outcome or cost, partners are notified immediately.