Joint Evaluation (Jo.E): A Collaborative Framework for Rigorous Safety and Alignment Evaluation of AI Systems Integrating Human Expertise, LLMs, and AI Agents
Bibliographic record
Abstract
The increasing sophistication of Artificial Intelligence (AI) systems necessitates a rigorous, multi-dimensional evaluation paradigm that surpasses conventional automated metrics and subjective human assessments. This paper introduces Jo.E (Joint Evaluation), a structured evaluation framework that integrates human expertise, AI agents, and Large Language Models (LLMs) to systematically assess AI systems across critical dimensions: accuracy, robustness, fairness, and ethical compliance. Building on methodologies such as "Agent-as-a-Judge" and "LLM-as-a-Judge", Jo.E provides a principled approach to identifying and mitigating AI risks through a tiered evaluation process. We validate this framework through controlled experiments on commercial models (GPT-40, Llama 3.2, and Phi 3), demonstrating its capacity to detect model vulnerabilities that single-method evaluations miss. The framework’s key innovation lies in its structured information flow between evaluation tiers, enabling targeted human expert involvement where automated methods are insufficient. This creates a scalable, reproducible evaluation methodology with comprehensive coverage of critical AI safety dimensions. Our experimental results show that Jo.E successfully identified 22% more adversarial vulnerabilities and 18% more ethical concerns than standalone evaluation approaches while reducing human expert time requirements by 54%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.165 | 0.246 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".