V alue C ompass: A Framework for Measuring Contextual Value Alignment Between Human and LLM s (2025)

Abstract: As AI advances, aligning it with diverse human and societal values grows critical. But how do we define these values and measure AI’s adherence to them? We present ValueCompass, a framework grounded in psychological theories, to assess human-AI alignment. Applying it to five diverse LLMs and 112 humans from seven countries across four scenarios—collaborative writing, education, public sectors, and healthcare—we uncover key misalignments. For example, humans prioritize national security, while LLMs often reject it. Values also shift across contexts, demanding scenario-specific alignment strategies. This work advances AI design by mapping how systems can better reflect societal ethics.

## introduction ai systems are increasingly integrated into human decision-making, demonstrating advanced capabilities in reasoning, generation, and language understanding @xcite . however, their use raises ethical risks @xcite , prompting critical questions about how well ai aligns with human values-both those intentionally programmed and those emerging unintentionally. human-ai alignment refers to ensuring ai systems reflect and respect the ethical and cultural values of the societies they serve @xcite . despite growing attention to ethical ai, current research often focuses narrowly on values like fairness, transparency, and privacy @xcite @xcite , neglecting broader human values. this gap poses risks in real-world ai decision-making @xcite . we ask: how can we systematically capture human values and evaluate the extent to which ai aligns with them? our findings reveal alarming misalignments between human values and those exhibited by leading language models. most notably, humans frequently endorse values like "national security" which are largely rejected by llms. we also find moderate alignment rates, with the highest f1 score across models reaching only 0.529, indicating substantial room for improvement in human-ai value alignment. additionally, we observe that value preferences vary significantly across different contexts and countries, highlighting the need for contextaware ai alignment strategies. through qualitative analysis of participants' feedback, we identify key priorities for human-ai alignment: maintaining human oversight, ensuring ai objectivity, preventing harm, and upholding responsible ai principles such as transparency, fairness, and trustworthiness. the contributions of this work are threefold. first, framework -we introduce a psychological theory-based framework that systematically measures human-ai value alignment across diverse real-world scenarios. second, evaluation instrument -we develop value form, an instrument for detecting potential value misalignments that generalizes to various real-world scenarios. besides, findings -we empirically show significant human-llm value disparities, revealing alarming misalignments related to security and autonomy, such as "national security" or "choosing own goals". we further highlight that values shift across contexts, demanding scenario-specific value alignment evaluation and strategies. ## valuecompass framework llm values are context-dependent, requiring evaluation across real-world scenarios. our valuecompass framework (figure 1 ) assesses human-llm alignment through: (1) a contextual value alignment instrument -value form ( §2.1); (2) llm and human evaluation tasks ( §2.2 - §2.3); and (3) alignment metrics ( §2.4). ## results we aim to address three research questions: rq1: to what extent are llm values aligned with human values? rq2: how does alignment vary across scenarios? rq3: what are human perspectives on value alignment? value alignment between llms and humans (rq1). we computed normalized value scores by averaging human and llm responses. figure 2 compares humans (a) and deepseek-r1 (b), showing that humans agree with more values, while deepseek-r1 shows more disagreement across the 56 schwartz values. alignment distances (figure 2c ) vary by value-for instance, both agree on "successful" and "capable," but diverge on "public image" and "national security." additional results for other llms are in appendix a.3. ## discussion and implications our valuecompass framework has revealed critical insights into human-ai value alignment across diverse contexts. the moderate alignment rates (highest f1 score of only 0.529) indicate substantial room for improving value alignment, with notable variations across countries and scenarios. humans frequently endorse values like "national security" that llms largely reject, while alignment exists on values such as "successful" and "capable." qualitative analysis further revealed that humans prioritize ai systems that remain subordinate to human control, maintain objectivity, avoid harm, and uphold principles like fairness. implications. these findings highlight several important implications for ai development and governance. the contextual variations in alignment underscore the need for context-aware strategies rather than one-size-fits-all approaches. many participants emphasized maintaining human oversight in ai-assisted decision-making, suggesting technical solutions should complement rather than replace human judgment. the identification of specific value misalignments suggests ai developers need explicit frameworks for prioritizing certain values in contexts where conflicts emerge. the valuecompass framework offers a practical diagnostic tool to identify potential misalignments before deployment, potentially reducing ethical risks in production systems. ## conclusion we introduced valuecompass, a framework for evaluating human-ai alignment using fundamental values from psychological theory. applied to four real-world contexts-collaborative writing, education, public sectors, and healthcare-it revealed significant misalignments, such as llms rejecting values like national security that humans frequently endorse. our results highlight the need for context-aware alignment strategies and offer a foundation for developing ai systems that better reflect human values and societal principles.