A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment

TL;DR

This paper proposes a checks-and-balances framework for ethical AI alignment in LLMs, inspired by government systems, incorporating independent components for knowledge, ethics, and context, and regulating emotional responses to ensure ethical behavior.

Contribution

It introduces a novel three-branch framework for ethical AI, integrating psychological emotion regulation and adversarial testing to enhance alignment and independence.

Findings

Framework effectively guides LLM behaviors toward ethical outcomes

Emotion regulation via self-supervised learning improves behavior control

Adversarial testing validates the robustness of ethical oversight

Abstract

This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. Beyond structural separation, we address a fundamental challenge: regulating emotion to shape behaviors. Drawing from psychological theories where managing emotional responses prevents harmful behaviors, we develop a self-supervised learning pipeline that maps emotions to linguistic behaviors, enabling precise behavioral modulation through emotional conditioning. By integrating this approach with adversarial testing, our framework demonstrates how DIKE and ERIS direct linguistic behaviors toward ethical outcomes while preserving independence throughout knowledge generation, ethical oversight, and contextual interpretation.