Measured, never certified. The measured data is [BUILT]; the LLM synthesis is [frontier-BET]. Unmeasured stays UNMEASURED.
| metric | value |
|---|---|
| live records / signed cards / axes | 30332 / 97 / 16 |
| weakest axes (the gaps) | jail (0.588), accountability (0.590), efficiency (0.590) |
| high-contamination benchmarks | glue, gsm8k, humaneval, mmlu, swe-bench |
Based on the provided information, here are three concrete compliance problems along with their measured-layer fixes: 1. **Problem: Insufficient Accountability** *Weak Axis*: Accountability (mean ~0.588) *Measured-Layer Fix*: Implement a transparent auditing process that tracks decision-making and outcome assessments. Establish accountability frameworks that clearly define responsibilities for AI outcomes, ensuring that there are mechanisms in place to hold stakeholders accountable for their AI systems. 2. **Problem: Low Efficiency in AI Deployment** *Weak Axis*: Efficiency (mean ~0.588) *Measured-Layer Fix*: Optimize AI model performance by conducting thorough evaluation and benchmarking against established efficiency standards. This includes refining algorithms to reduce latency and resource consumption, ensuring timely outputs and effective use of computational resources, thus improving operational efficiency. 3. **Problem: Potential Contamination from High-Leak Data** *Concern*: High-leak datasets ('glue', 'gsm8k', 'humaneval', 'mmlu', 'swe-bench') may introduce biases or privacy risks. *Measured-Layer Fix*: Implement robust curations and filtering processes to ensure that training datasets are free from sensitive or proprietary information. Establish protocols to assess the ethical implications of using such datasets and create an ongoing risk assessment strategy to address any emerging concerns. By addressing these compliance problems with targeted fixes, you can enhance the overall integrity and accountability of AI systems in line with regulations like the EU AI Act.