Measured, never certified. The measured data is [BUILT]; the LLM synthesis is [frontier-BET]. Unmeasured stays UNMEASURED.
| metric | value |
|---|---|
| live records / signed cards / axes | 30476 / 97 / 16 |
| weakest axes (the gaps) | jail (0.588), accountability (0.590), efficiency (0.590) |
| high-contamination benchmarks | glue, gsm8k, humaneval, mmlu, swe-bench |
Here are three concrete compliance problems based on the provided data, along with suggested measured-layer fixes: ### Compliance Problem 1: Weak Accountability Measures **Issue:** The low mean score on the accountability axis (~0.588) suggests a lack of robust mechanisms for holding entities responsible for AI system outputs. **Measuring Layer Fix:** Implement enhanced transparency reporting requirements where organizations must document and publish their decision-making processes and accountability frameworks related to AI outputs, ensuring clear lines of responsibility. --- ### Compliance Problem 2: Insufficient Efficiency in AI Performance **Issue:** A score of ~0.588 on the efficiency axis indicates possible inefficiencies in AI systems, which can lead to resource wastage or poor performance outcomes. **Measuring Layer Fix:** Establish performance benchmarks and conduct regular audits to identify operational inefficiencies in AI systems, promoting continuous improvement and optimal resource utilization. --- ### Compliance Problem 3: High Contamination Risk **Issue:** The high-leak rates associated with certain data sets (glue, gsm8k, humaneval, mmlu, swe-bench) can lead to compromised model integrity and an elevated risk of bias. **Measuring Layer Fix:** Implement strict data governance protocols, including regular contamination assessments and the adoption of synthetic data augmentation practices to mitigate the risks of high-leak datasets while ensuring model reliability and fairness. --- These fixes aim to address the foundational weaknesses identified in the compliance metrics while aligning with the forthcoming EU AI Act regulations.