Responsible AI
Ethics, trust, safety, legality, and transparency in AI development and deployment. It's not just a philosophy—but a practical engineering challenge requiring real tools.
Building Trust Through Technical Excellence
Responsible AI encompasses ethics, trust, safety, legality, and transparency in AI development and deployment. Beyond ethical ideals, responsible AI requires robust technical frameworks, transparent governance, and global collaboration to build systems that are trustworthy, equitable, and aligned with human values.
Key Frameworks & Tools
Responsible Generative AI Framework (RGAF)
A community-led initiative by the Generative AI Commons, offering a vendor-neutral standard for building trustworthy AI systems.
9 Core Dimensions:
12 Genres of Responsible AI Tools
Red Hat's taxonomy of tools that address ethical, technical, and social challenges of deploying AI systems responsibly.
Measure behavior and abilities of models
Why models make decisions
Moderate interactions between users, agents, and models
Quantify and compare outcomes between input groups
Probe models for exploitable weaknesses
Real-World Applications
Trust Infrastructure (India)
iSprit's Directed Identity Graphs (DIG) framework enables scalable, federated identity management for child safety and AI governance.
AI Governance (FINOS)
Machine-readable standards for AI governance in financial services, mapping threats to mitigations and legal obligations.
Multimedia Standards (ITU)
Global AI standards for emergency care and multimedia applications, bridging technical, policy, and communication gaps.
Join the Responsible AI Movement
Contribute to frameworks, tools, and standards that ensure AI serves humanity's best interests.