Technology
Create ecosystem partnerships that collaborate on technical design recommendations to influence ethical AI policies and recommendations
Design decisions made today become the defaults billions of people live with tomorrow.
AI systems are being built faster than the frameworks meant to guide them. Every choice about training data, fairness metrics, or human oversight encodes values into products that shape how people access healthcare, apply for jobs, or interact with the justice system.
What We're Seeing
The gap between AI deployment and ethical governance is widening.
- 78% of organizations use AI in at least one business function.¹ Only 18% have an enterprise-wide responsible AI governance council.²
- Practitioners want to do the right thing but face overwhelming, contradictory guidance from dozens of sources.
- Bias remains measurable and persistent.
- Three competing regulatory approaches (EU, US, Asia-Pacific) are fragmenting rather than converging.
What We Do
We turn ethical principles into technical design recommendations practitioners can actually use.
- Convene AI labs, standards bodies, and technical working groups to build consensus definitions for ethical AI and human-centered design.
- Synthesize the major global frameworks (IEEE, ISO, NIST, OECD) into unified guidance that works across jurisdictions.
- Track what is actually working in alignment research, bias detection, and interpretability.
- Translate fairness requirements across domains, because what counts as fair in healthcare differs from employment or criminal justice.
- Connect frontier safety research to practical deployment guidance for teams beyond the largest AI labs.
What Changes Because of This
Teams move from paralysis to clarity.
- Clear, contextualized guidance matched to deployment scenarios, not dozens of overlapping frameworks to navigate alone.
- Design recommendations embedded where technical decisions actually happen, not buried in policy documents.
- Unified frameworks that translate across jurisdictions, not fragmented regional compliance paths.
- Emerging safety research made accessible to any team building AI systems, not locked in academic papers.
Who We Work With
Technical teams making consequential design decisions.
AI research labs. Standards bodies. MLOps and product teams implementing fairness and accountability. AI safety researchers. Technical policymakers shaping governance.
Not for those who believe the right response to AI risk is prohibition. We believe AI can serve humanity, and that requires embedding ethics into technical architectures from the start.
Key Issues
- Privacy risks and expanded identification capabilities (e.g., face/voice ID, re-identification)
- Potential failures in safety-critical contexts (medicine, transport, infrastructure)
- Infrastructure and resilience concerns (grid demand, vendor dependence, supply-chain brittleness)
- Research integrity challenges (fabricated citations/data; reproducibility issues)
- Data governance and security gaps (leaks, weak controls, shadow datasets)
- AI Alignment/control uncertainties (unintended behavior; reward-seeking edge cases)
- Human negative impact due to lack of focus on human-centered design, AI ethics and responsible deployment
Key Objectives
• A global definition of Ethical AI to include "do no harm", "fairness for all", and “sanctity of human values“
• Ongoing meta-research of AI advancements, technical practices, bias algorithms and model training trends as it pertains to ethical AI Alignment.
• AI Alignment refers to the process of ensuring that artificial intelligence systems act in ways that are aligned with human values,goals, and intentions
• Identification of labs, institutions and technologies shaping the AI landscape
• Global leaders forum for discussion of AI Alignment
• Track trends in key technology aspects:
• AI and Ethics
• Human-centric AI Design
• Emotional Intelligence in Technology
• Responsible AI Deployment