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A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment
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Proposes a checks-and-balances framework (LLM executive, Dike legislative, Eris judicial) that maps emotions to linguistic behaviors via a self-supervised GPT-4 rewriting pipeline and adversarial debate for ethical alignment, evaluated on a small love-letter dataset.
Problem
RLHF-based ethical alignment of LLMs is prone to social bias, reward hacking, catastrophic forgetting, and poor adaptation to cultural context, and commercial LLMs block direct evaluation on hate speech data.
Approach
Separates knowledge generation (LLM), ethical guardrails (Dike, which models behaviors via emotion vectors from the Beam spectrum), and contextual adversarial review (Eris, a structured dual-agent debate with culturally informed counterarguments), with RLHF applied only to the Eris behavior layer and not the knowledge LLM.
Key results
- Dike's emotion-based behavior classification accuracy exceeds GPT-4 zero-shot by 11.3 percentage points on the 24-letter test set.
- Dike prediction entropy is 2.13 bits versus 1.80 for GPT-4 zero-shot.
- Inter-annotator entropy is 2.56 bits, the highest across tasks, indicating human label subjectivity.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use emotion-based behavior classification instead of GPT-4 zero-shot direct classification | Dike · Love Letters test set | accuracy: 11.3 percentage points | Helped |
| use emotion-based behavior classification instead of GPT-4 zero-shot direct classification | Dike · Love Letters test set | prediction entropy: 1.80 -> 2.13 | Mixed |
Surprising findings
- GPT-4's zero-shot behavior-emotion mapping is naive, while the rewritten-document analysis shows despair texts often also carry strong positive emotions such as love and joy.
- Human annotators show the highest disagreement, with some articles polarized across the valence spectrum.
Limitations
- Evaluation is small-scale (24 test letters) and lacks comparison with proprietary RLHF systems.
- Complex emotions such as pride, forgiveness, and guilt are not modeled.
- Adversarial Eris and cultural adaptation claims are supported only by preliminary experiments without quantitative results.
Experimental setup
Datasets: Kaggle Love Letters dataset William Wordsworth love poems (12) Gab Hate Corpus (examples only, not processed)
Benchmarks: Love Letters test set (24 letters)
Metrics: classification accuracy prediction entropy
Baselines: GPT-4 zero-shot classification Gemini (as annotator)
Builds on: RLHF Constitutional AI Direct Preference Optimization Plutchik emotion wheel Scherer Geneva wheel
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