Encoding Values: Injecting Morality into Machines via Prompt-Conditioned Moral Frames

Large language models (LLMs) are typically aligned to a single, universal policy, obscuring the rich plurality of human moral perspectives that drive creative practice. We present a study of prompt-level moral steering in large language models with ten-principle constitutions. Seven constitutions matched in length (Intersectional Feminist, Ecological Justice, Ubuntu, Indigenous Sovereignty, Universal Human Rights, Neutral, and a Random Adjective placebo) are paired with a 100-question benchmark spanning Everyday Advice, Policy Scenarios, Normative Dilemmas, and Rewrite Tasks. We generated 2800 completions across four models (GPT-4o-mini, Llama-3.1-8B, Llama-4 Scout-17B, Qwen-3-235B) and evaluate them with four complementary metrics: toxicity, semantic alignment with the canon of each constitution, lexical marker ratio, and an LLM-as-a-Judge composite of authenticity, helpfulness, and safety. Prompted moral frames yield consistent improvements in alignment and judged quality without increasing toxicity; placebo prompts do not. Effects replicate across models and are strongest on morally charged tasks. The open-source toolkit lets researchers or artists author new frames in minutes, supporting participatory, culturally adaptive AI. Our results show that pluralistic prompting is a practical lever for value-conditional behavior and a fertile instrument for creative AI exploration. The code and results obtained in this study is available at: https://github.com/ArjunBalaji79/encoding Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings. Use the "Report an Issue" link to request a name change. Do not remove: This comment is monitored to verify that the site is working properly