Ensuring that LLMs align with human values is not an easy task. Alignment is particularly challenging because human values are not static, universal, or easily quantifiable and codifiable. What is considered ethical, fair, or appropriate varies significantly across cultures, political ideologies, and social contexts, making it difficult to establish a one-size-fits-all alignment approach (Liu et al., 2023; Shen et al., 2023). An output considered neutral or factual in one country might be seen as biased or controversial in another, specifically when thinking about political values like democracy. Similarly, ethical priorities and dilemmas like whether AI should prioritise free speech or harm prevention are often in direct conflict with one another.
Hence, alignment is not just a technical issue but is also deeply political, sociological, and philosophical. Since LLMs lack an inherent moral framework, they risk reinforcing biases, misinterpreting cultural nuances, or making implicit value judgments that unintentionally favour certain perspectives over others. Addressing this challenge requires constant oversight, diverse and representative training data, and adaptation to societal norms. The goal of alignment is not to impose a fixed ethical standard, but rather to navigate the complexities of ethical decision-making, such as considerations for fairness, inclusivity, and adaptability, while minimising harm in a given context.
Mastering this crucial challenge of aligning LLMs with human expectations and ethical standards requires training strategies, human feedback loops, and reinforcement learning techniques. One of the most widely used methods is Reinforcement Learning from Human Feedback (RLHF), where human reviewers evaluate the model’s outputs, ranking them and guiding the system toward more accurate, safe, and contextually appropriate responses (Liu et al., 2023; Shen et al., 2023). Another key approach is instruction tuning, where models are fine-tuned using carefully curated datasets that emphasise desirable behaviours. This involves filtering out harmful, misleading, or biased content while incorporating representative data to enhance fairness and reliability. Researchers are also experimenting with contrastive fine-tuning, where models are trained on correct and incorrect examples, helping them distinguish between preferable and undesirable outputs (Martineau, 2023).
Despite these advancements, alignment remains an evolving and deeply complex challenge that urgently warrants further research to refine existing alignment methods and to develop scalable, adaptable, and ethically grounded approaches that ensure LLMs operate in a way that truly reflects human values. This is exactly why collaborative, cross-sectoral projects like alignAI are urgently needed. The alignAI doctoral network brings together expertise from technical AI development, social sciences, and ethics to develop alignment strategies that are practical, transparent, and socially responsible. With a strong focus on explainability and fairness, the project explores real-world applications in education, mental health, and media consumption, ensuring that AI systems serve human needs while mitigating bias and reinforcing ethical accountability.
The Future of Alignment
As LLMs continue to scale in power and influence, alignment becomes more complex. Future research is expected to move toward self-improving alignment techniques, where models refine their own behaviour over time while remaining subject to external oversight (Liu et al., 2023; Shen et al., 2023). There’s also a growing demand for explainability and transparency, ensuring that users can scrutinise AI decisions rather than blindly having to accept them.
At the same time, governments and regulatory bodies are stepping in to shape AI governance, with policies like the EU AI Act proposing stricter oversight for high-risk applications. As alignment strategies advance, the debate won’t just be about how to align AI but also who decides what alignment should look like.
Ultimately, LLMs are more than just technological tools; they significantly reshape how we communicate, access knowledge, and interact with digital systems. While LLMs offer remarkable capabilities, their potential risks necessitate diligent alignment efforts. Ensuring they align with human values, ethical considerations, and societal needs is not just a technical challenge but a necessity. The alignment problem is far from solved, but as research progresses, so does our ability to steer these systems in the right direction, whatever that might end up meaning.
References:
Liu, Y., Yao, Y., Ton, J., Zhang, X., Guo, R., Cheng, H., Klochkov, Y., Taufiq, M. F. & Li, H. (2023). Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models’ Alignment. arXiv.org. https://arxiv.org/abs/2308.05374
Martineau, K. (2024). What is AI alignment? IBM Research. https://research.ibm.com/blog/what-is-alignment-ai
Shen, T., Jin, R., Huang, Y., Liu, C., Dong, W., Guo, Z., Wu, X., Liu, Y. & Xiong, D. (2023). Large Language Model alignment: a survey. arXiv.org. https://arxiv.org/abs/2309.15025
Further reading/watching/listening:
Books & Articles:
Tennant, E., Hailes, S. & Musolesi, M. (2024, 2. Oktober). Moral Alignment for LLM Agents. arXiv.org. https://arxiv.org/abs/2410.01639
Russell, S. (2020). Human compatible: Artificial Intelligence and the Problem of Control. National Geographic Books.
Wiener, N. (1960). Some Moral and Technical Consequences of Automation. Science, 131(3410), 1355–1358. https://doi.org/10.1126/science.131.3410.1355
Videos & Podcasts:
“The Alignment Problem” Brian Christian talks at Yale (2022)
“OpenAI’s huge push to make superintelligence safe” 80 000 Hours Podcast with Jan Leike (2023)