Sushegaad/Responsible-AI-Model-Evaluations

This project is a very early-stage (1 star, <10 days old) implementation of an LLM-as-a-judge safety benchmark. While the mapping to the NIST AI Risk Management Framework is a useful organizational layer, the underlying technical approach—using one model (Haiku) to score the responses of others—is a standard industry pattern with no inherent moat. The project faces extreme competition from several directions: 1) Frontier labs themselves (OpenAI Evals), 2) Specialized AI safety startups (Giskard, Deepchecks, Robust Intelligence) that have raised millions and possess deeper proprietary red-teaming datasets, and 3) Cloud providers (Azure AI Studio, AWS Bedrock) which are increasingly integrating NIST-aligned governance and safety dashboards directly into their platforms. Without a unique, massive adversarial dataset or a first-mover advantage in a specific regulatory niche, this project remains a personal experiment rather than a defensible product. The 'displacement horizon' is short because official AI Safety Institutes (US/UK) are currently defining the standards that will likely render independent, small-scale benchmarks obsolete.