Five Pillars of AI Governance
Our work spans the full landscape of AI quality control — from technical benchmarking to regulatory translation to the societal implications of deployed systems.
01
AI Quality Control & Standards
Establishing what it means for an AI system to behave as specified.
Quality control in AI requires more than accuracy metrics. It demands systematic verification of behavior across capability, safety, reliability, and alignment dimensions — under distribution shift, adversarial conditions, and edge cases that standard benchmarks do not capture. We develop open testing protocols, evaluation frameworks, and certification criteria that any organization can apply and any researcher can scrutinize. Our standards work engages directly with ISO/IEC JTC 1/SC 42, NIST, and emerging national AI standards bodies.
Key Outputs
- Open evaluation benchmarks
- Testing protocol specifications
- Certification criteria frameworks
- Standards body engagement
02
Regulatory Compliance
Translating regulation into actionable compliance architectures.
The regulatory landscape for AI is evolving rapidly — the EU AI Act, NIST AI RMF, ISO/IEC 42001, and a growing body of sector-specific guidance create a complex compliance environment that most organizations lack the capacity to navigate. We translate these frameworks into practical compliance architectures: risk classification methodologies, documentation requirements, conformity assessment procedures, and audit-ready governance structures. Our work is designed for organizations of every scale, from large enterprises to research institutions to public sector bodies.
Key Outputs
- Compliance architecture templates
- Risk classification tools
- Regulatory mapping guides
- Audit readiness frameworks
03
Fairness in Social Media
Building audit frameworks for algorithmic platform accountability.
Social media platforms deploy AI systems at a scale and societal impact that few other deployment contexts match. Recommendation algorithms, content moderation systems, and ad-targeting infrastructure shape public discourse, political participation, and individual opportunity in ways that are poorly understood and rarely audited. We develop methodologies for external algorithmic auditing of platform AI, working with civil society organizations, regulators, and researchers to establish what platform accountability actually requires — and what evidence is necessary to assess it.
Key Outputs
- Algorithmic audit methodologies
- Platform accountability frameworks
- Civil society audit toolkits
- Regulatory engagement briefs
04
Edge-Device & Cross-Silo Ecosystems
Governance for AI where centralized oversight is structurally impossible.
AI deployed at the edge — on devices, in vehicles, in medical equipment, in industrial systems — and across organizational silos presents governance challenges that centralized oversight models cannot address. Data cannot be aggregated; models cannot be inspected from a single vantage point; accountability is distributed across supply chains and jurisdictions. We develop governance frameworks specifically designed for these contexts: federated audit protocols, cross-organizational accountability structures, and standards for AI behavior verification in distributed deployment environments.
Key Outputs
- Federated audit protocols
- Cross-silo governance frameworks
- Edge deployment standards
- Supply chain accountability models
05
Privacy Enhancing Technologies
Privacy as infrastructure, not afterthought.
Privacy enhancing technologies — federated learning, differential privacy, secure multi-party computation, homomorphic encryption — are not merely compliance tools. They are the technical foundation for AI systems that can learn from sensitive data without exposing it, collaborate across organizational boundaries without sharing raw information, and be audited without revealing proprietary details. We advance PETs as first-class components of responsible AI infrastructure: developing implementation standards, evaluating privacy-utility trade-offs, and building the practitioner knowledge base needed for widespread adoption.
Key Outputs
- PET implementation standards
- Privacy-utility evaluation frameworks
- Practitioner guidance documents
- Open-source tooling contributions