PRISM: Perspective Reasoning for Integrated Synthesis and Mediation as a Multi-Perspective Framework for AI Alignment
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
In this work, we propose Perspective Reasoning for Integrated Synthesis and Mediation (PRISM), a multiple-perspective framework for addressing persistent challenges in AI alignment such as conflicting human values and specification gaming. Grounded in cognitive science and moral psychology, PRISM organizes moral concerns into seven "basis worldviews", each hypothesized to capture a distinct dimension of human moral cognition, ranging from survival-focused reflexes through higher-order integrative perspectives. It then applies a Pareto-inspired optimization scheme to reconcile competing priorities without reducing them to a single metric. Under the assumption of reliable context validation for robust use, the framework follows a structured workflow that elicits viewpoint-specific responses, synthesizes them into a balanced outcome, and mediates remaining conflicts in a transparent and iterative manner. By referencing layered approaches to moral cognition from cognitive science, moral psychology, and neuroscience, PRISM clarifies how different moral drives interact and systematically documents and mediates ethical tradeoffs. We illustrate its efficacy through real outputs produced by a working prototype, applying PRISM to classic alignment problems in domains such as public health policy, workplace automation, and education. By anchoring AI deliberation in these human vantage points, PRISM aims to bound interpretive leaps that might otherwise drift into non-human or machine-centric territory. We briefly outline future directions, including real-world deployments and formal verifications, while maintaining the core focus on multi-perspective synthesis and conflict mediation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 4 of its 94 references that are in the map. First posted on arXiv 5 Feb 2025.
Builds on
4 of 94 references.
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Other references · 3
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2016Concrete Problems in AI Safetycited by 1,582
- 2024Deliberative Alignment: Reasoning Enables Safer Language Modelscited by 292
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Citing papers per year: 2025: 2.
2 papers.
- 2025Normative Moral Pluralism for AI: A Framework for Deliberation in Complex Moral Contexts10 Aug 2025
- 2025Towards Integrated Alignment8 Aug 2025
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- 2026Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignment
- 2026LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
- 2023A Multi-Level Framework for the AI Alignment Problem