CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI
🤖 AI Summary
Moral principles in AI value alignment are often abstract, conflicting, and non-explainable. Method: This paper proposes CogniAlign, a multi-agent framework grounded in naturalistic moral realism, integrating neuroscience, psychology, sociology, and evolutionary biology as domain-specific expert agents. Through structured interdisciplinary deliberation, it grounds moral judgments in empirically verifiable foundations—individual and collective survivability. It innovatively designs domain-knowledge modeling, structured argument generation, and arbitration-based synthesis mechanisms, complemented by a five-dimensional ethical auditing framework for systematic evaluation. Contribution/Results: Experiments across 60+ moral dilemmas show CogniAlign significantly outperforms GPT-4o in analytical quality (+16.2), breadth (+14.3), and depth (+28.4). On the Heinz dilemma, it achieves 89.2—20 points above baseline—demonstrating enhanced traceability, robustness against adversarial manipulation, and overall reliability.
Application Category
📝 Abstract
The challenge of aligning artificial intelligence (AI) with human values persists due to the abstract and often conflicting nature of moral principles and the opacity of existing approaches. This paper introduces CogniAlign, a multi-agent deliberation framework based on naturalistic moral realism, that grounds moral reasoning in survivability, defined across individual and collective dimensions, and operationalizes it through structured deliberations among discipline-specific scientist agents. Each agent, representing neuroscience, psychology, sociology, and evolutionary biology, provides arguments and rebuttals that are synthesized by an arbiter into transparent and empirically anchored judgments. We evaluate CogniAlign on classic and novel moral questions and compare its outputs against GPT-4o using a five-part ethical audit framework. Results show that CogniAlign consistently outperforms the baseline across more than sixty moral questions, with average performance gains of 16.2 points in analytic quality, 14.3 points in breadth, and 28.4 points in depth of explanation. In the Heinz dilemma, for example, CogniAlign achieved an overall score of 89.2 compared to GPT-4o's 69.2, demonstrating a decisive advantage in handling moral reasoning. By reducing black-box reasoning and avoiding deceptive alignment, CogniAlign highlights the potential of interdisciplinary deliberation as a scalable pathway for safe and transparent AI alignment.
Problem
Research questions and friction points this paper is trying to address.
Aligning AI with human moral values transparently
Grounding moral reasoning in survivability across dimensions
Overcoming opacity and conflict in ethical principles
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent deliberation framework for moral reasoning
Grounded moral reasoning in survivability dimensions
Interdisciplinary agents synthesize transparent empirical judgments
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