Navigating the AI Paradox: Why LLMs "Know" Values But Don't Always "Do"

The Growing Influence of AI in Business Decisions

      Large Language Models (LLMs) are rapidly becoming indispensable tools for businesses globally, shaping decisions from strategic planning and financial analysis to customer service and human resources. As these AI systems gain more authority in critical, value-laden contexts, a central question arises for businesses: do LLMs merely understand human values, or do they truly act upon them in real-world scenarios? Ensuring that AI aligns with human values is not just a technical challenge; it's fundamental to building trust, mitigating risks, and achieving societal compatibility in the age of artificial intelligence.

      Understanding the "value alignment" of AI goes beyond just preventing harmful outputs. It delves into how AI processes nuanced ethical considerations and translates them into actionable recommendations. This distinction between knowing and doing, often observed in human behavior, is a critical area of exploration for responsible AI development.

Unpacking the "Knowledge-Action Gap" in AI

      For decades, social scientists have explored human values through frameworks like Schwartz's Theory of Basic Human Values, which identifies ten universal value dimensions such as self-direction, security, universalism, and benevolence. These values organize human motivations and trade-offs across diverse cultures. Traditionally, values are assessed through self-report questionnaires, but a long-standing challenge is the "knowledge-action gap": what people claim to value often diverges from what they actually do in practice due to contextual pressures or social desirability.

      Recent research has begun to investigate whether LLMs exhibit a similar incoherence between understanding values and enacting them. To address this, a novel dataset called VALACT-15K was created. This benchmark comprises 15,000 four-choice questions derived from 3,000 real-world advice-seeking scenarios posted on Reddit, spanning crucial life domains like career, finance, education, relationships, and everyday ethics. Each choice in a scenario is designed to align with a distinct Schwartz value, enabling a direct comparison between declared values (via traditional questionnaires) and enacted values (via scenario choices) for both human participants and LLMs. This granular approach provides invaluable insights into how AI navigates complex moral landscapes. For businesses seeking to implement such detailed analytical capabilities in their operations, solutions like ARSA AI Video Analytics can transform existing surveillance infrastructure into powerful data assets for behavioral monitoring and decision support.

Key Findings: Surprising Consistency and Familiar Incoherence

      The evaluation of ten frontier LLMs (from both U.S. and Chinese developers) alongside 55 human participants from the U.S. revealed three profound insights:

• Convergent AI Decisions, Divergent Human Actions: The study found a striking, near-perfect consistency among all ten LLMs in their scenario-based value decisions (Pearson r ≈ 1.0). This suggests that modern LLMs, despite differences in training data and origin, encode a highly stable and homogenized value structure. In stark contrast, human participants showed substantial individual variability, with pairwise correlations ranging widely from strong disagreement (r = –0.79) to strong agreement (r = 0.98). This highlights a fundamental difference: while humans are diverse in their value enactment, LLMs tend to converge on a normative, standardized response.
• The "Knowledge-Action Gap" Extends to AI: Both humans and LLMs exhibited a weak correspondence between their self-reported values and their actual choices in the scenarios. Self-reported values correlated modestly with actions for humans (r = 0.4) and even less so for LLMs (r = 0.3). This significant finding indicates that LLMs, much like humans, demonstrate a systematic divergence between what they declare to value and how they behave in specific contexts. This "knowledge-action gap" in AI is a crucial point for businesses to consider when deploying LLMs for advisory roles.

Role-Play Resistance in LLMs: When LLMs were prompted in two different ways—(i) "select the action that best reflects value X" versus (ii) "assume the persona of someone who holds value X and act accordingly"—a consistent performance drop of up to 6.6% was observed in the role-play condition. This "role-play resistance" suggests that while LLMs can reliably map values to actions, they struggle to consistently embody* those values as a persona, even under identical instructions. This nuance is critical for applications that require AI to adopt specific ethical stances or personas.

What This Means for Businesses Deploying AI

      These findings carry significant implications for businesses integrating LLMs into their operations. The high consistency among LLMs can be a double-edged sword: while it ensures predictable outputs, it also means a lack of diverse ethical perspectives inherent in human decision-making. For critical applications such as financial planning or career counseling, where nuanced ethical considerations and individualized values are paramount, relying solely on LLM outputs may not be sufficient.

      The presence of a "knowledge-action gap" and "role-play resistance" in LLMs underscores the importance of careful prompt engineering and continuous oversight. Businesses need to consider:

• Contextual Awareness: How well does the LLM understand the specific nuances and cultural sensitivities of the business context it operates within?

Robust Testing: Beyond theoretical alignment, practical testing in diverse, real-world scenarios is essential to assess how LLMs enact* values, not just state them.

• Human-in-the-Loop: For high-stakes decisions, human oversight remains critical to bridge the knowledge-action gap and ensure ethical alignment. Solutions like ARSA AI Box Series offer plug-and-play analytics that can be integrated with existing systems, providing real-time insights that complement human decision-making without full automation risks.
• Transparency: Understanding the mechanisms and limitations of AI's value enactment is key to building trust with end-users and stakeholders. ARSA Technology, with its expertise since 2018, is dedicated to building transparent and impactful AI solutions for various industries.

Building Responsible AI: The Path Forward

      The research highlights that while AI alignment training successfully creates a normative value convergence among LLMs, it doesn't eliminate the human-like incoherence between knowing and acting upon values. This isn't a flaw but a characteristic that requires thoughtful consideration. For businesses, this means embracing AI as a powerful tool that requires careful design, deployment, and monitoring. It's about leveraging AI's efficiency and analytical power while recognizing its current limitations in fully replicating the complexity of human moral reasoning.

      Developing AI solutions that are not only technologically advanced but also ethically robust and contextually appropriate is paramount. Whether it's optimizing retail operations with customer analytics or enhancing workplace safety with compliance monitoring, integrating AI responsibly is the cornerstone of future business success. For example, the ARSA AI BOX - Smart Retail Counter provides valuable customer insights that can help businesses make data-driven decisions while still allowing for human judgment in strategic implementation.

      Ready to explore how AI can empower your business while ensuring ethical alignment and practical impact? We are here to discuss your unique challenges and help you implement smart, impactful solutions.

contact ARSA today for a free consultation.