Access our latest white papers, research findings, and technical standards on AI governance, safety, and responsible innovation.
Browse All TranscriptsOperationalizing the "Third Way": From Black Box Liability to Glass Box Trust. A concise 5-phase engineering framework to embed safety norms directly into AI models.
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A comprehensive discussion on implementing Constitutional AI frameworks, covering design defects in AI systems, disparate impact, and the roadmap for building trustworthy AI through glass box transparency.
Legal framework addressing how Constitutional AI addresses design defects in high-stakes AI systems. Essential reading for NIST AI RMF officials, policymakers, and AI risk engineers.
A comprehensive legal framework establishing strict liability for opaque AI systems and safe harbor for Constitutional AI. Proposes the California Algorithmic Accountability & Fairness Act (CA AAFA) to end algorithmic discrimination.
An urgent call to action examining why immediate implementation of Constitutional AI principles is critical for preventing catastrophic AI failures.
Exploring how AI systems trained on historical data inevitably reflect and amplify societal biases, and why Constitutional AI is necessary to break this cycle.
A comprehensive analysis of AI's current capabilities, challenges, and societal impact as we navigate the transformative year of 2025.
A comprehensive framework for understanding and addressing liability issues in AI deployment and decision-making.
Exploring how constitutional frameworks can guide AI development to ensure alignment with human values and democratic principles.
Examining the critical gap between technical AI alignment and practical risk engineering, and how Constitutional AI provides the necessary bridge.
Technical standards and best practices for implementing Constitutional AI principles in production systems.
Methodologies and frameworks for building trustworthy AI systems that maintain human oversight and accountability.
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