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MLCommons and AI Safety
Collaborative Effort
MLCommons, a collaboration between industry and academia, aims to enhance AI safety, efficiency, and accountability through rigorous measurement standards like MLPerf.
AI Safety Working Group and Benchmark Development
The AI Safety Working Group, established in late 2023, focuses on developing benchmarks for assessing AI safety and tracking progress over time. It aims to increase transparency and foster collective solutions to the challenges of AI safety evaluation.
Practical Solutions
The group has developed version 0.5 of the AI Safety Benchmark, evaluating safety risks associated with AI systems utilizing chat-tuned language models. The benchmark covers a taxonomy of 13 hazard categories with an openly accessible platform and downloadable tool called ModelBench for evaluating AI system safety.
Value and Audience
The benchmark targets model providers, integrators, and AI standards makers and regulators, providing a structured approach to evaluate safety risks and drive innovation in AI safety processes.
Results and Conclusion
The study evaluated AI systems utilizing chat-tuned language models against the benchmark, showing varying levels of risk across models. The v0.5 release of the AI Safety Benchmark offers a structured approach to evaluate safety risks and is a foundation for future iterations.
AI Solutions and Value Proposition
Practical AI Solutions
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Implementing AI Solutions
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