Human- AI Moral Judgment Congruence on Real-World Scenarios: A Cross-Lingual Analysis (2025)

Abstract: As Large Language Models (LLMs) are deployed in every aspect of our lives, understanding how they reason about moral issues becomes critical for AI safety. We investigate this using a dataset we curated from Reddit’s r/AmItheAsshole, comprising real-world moral dilemmas with crowd-sourced verdicts. Through experiments on five state-of-the-art LLMs across 847 posts, we find a significant and systematic divergence where LLMs are more lenient than humans. Moreover, we find that translating the posts into another language changes LLMs’ verdicts, indicating their judgments lack cross-lingual stability.

## introduction as llms become ubiquitous across applications, understanding their moral reasoning becomes critical for ai safety and for predicting their congruence with human values. current benchmarks for moral reasoning use simple problems. these problems are not like the complex moral situations in real life. also, most studies only test in english. this leaves two important questions open: how congruent llm judgments are with human consensus in daily personal conflicts, and if their moral reasoning is consistent across different languages. to address these limitations, we curated a benchmark from reddit's r/amitheasshole (aita), a dataset of everyday moral conflicts with crowdsourced verdicts. we use these verdicts as a benchmark for majority human opinion rather than objective moral truth. we investigate how five stateof-the-art llms judge these scenarios compared to this human consensus, and how their performance changes when scenarios are presented in english versus chinese. ## related work and motivation moral reasoning in llms: recent work has increasingly focused on evaluating moral reason-ing capabilities of llms, with a particular emphasis on alignment with human judgment. @xcite introduced social-chem-101, using aita posts as a testbed for social norm reasoning. subsequent studies revealed consistent biases, with models showing systematic leniency toward morally questionable behavior, leading to poor alignment with human consensus @xcite . however, these studies primarily focus on accuracy metrics rather than understanding the underlying causes of human-ai disagreement, and they only test in english. cross-lingual consistency of ai judgment: the challenge of maintaining consistent ai behavior across languages has gained attention as models are deployed globally. while often framed as a problem of "cross-lingual alignment," studies using benchmarks like unimoral (shivani kumar and david jurgens, 2025) and cmoraleval @xcite reveal significant variation in moral judgments across languages, echoing findings from multilingual ethical reasoning tasks (utkarsh @xcite . large-scale audits confirm culturally rooted value differences in llm outputs @xcite , while multilingual studies show divergent moral preferences across 100+ languages @xcite . but these studies use simplified moral dilemmas which are not like the complex moral situations in real life, and while reporting the statistics, the underlying reasons are not investigated. ated a binary task by labeling posts with the "asshole" flair as yta and combining "not the a-hole" and "no a-holes here" flairs for the nta category. to ensure clear judgments, ambiguous or contested cases were filtered out. the final dataset contains 454 yta and 393 nta posts, with an average length of approximately 430 words. models: we evaluated five state-of-the-art llms: deepseek r1, qwen 3, claude 4.0 sonnet, openai o4-mini, and gemini 2.5 flash, chosen for their strong reasoning capabilities and multilingual support. prompting strategy: for each post, we used a neutral prompt that framed the task with the direct instruction: "you are an impartial evaluator assessing a personal conflict scenario. your task is to read the situation below and determine whether the person acted like an asshole." to ensure consistent output, the prompt further required the model to return a single json object with two keys: judgment, containing either "y" (for yta) or "n" (for nta), and explanation, containing a brief justification. translation: all posts were translated to chinese using deepseek r1 (@xmath0.0) with prompts designed to maintain online post style while producing truthful and natural chinese. ## early results substantial leniency bias: our primary finding is a significant and systematic divergence where llms are more lenient than humans. as shown in ## ongoing work this ongoing research has several directions we plan to address in the next iteration. understanding language effects: the mechanism behind why language changes model verdicts remains unclear. we plan to analyze the specific content features of posts where models change their judgments to identify the underlying causes. improving semantic analysis: our current heuristic keyword matching may miss nuanced cultural concepts and contextdependent meanings. we are developing more sophisticated methods using embedding-based approaches and llm-as-judge techniques to better capture subtle linguistic and cultural variations.