How Different AI Models Learn to Reflect Human Values

1. Introduction: Claude 3 and the Real Breakthrough

Claude 3, Anthropic’s latest AI model, has captured the attention of the tech world with its performance. It is faster, more capable, and remarkably fluent in conversation. Yet the most important development is not the model’s intelligence, but the method behind it.

Claude 3 was not aligned using the standard approach of collecting thousands of human ratings. Instead, it was trained using a different method known as Constitutional AI. This approach is grounded in a simple but radical idea: rather than teaching AI through examples of what people like, we can teach it to reason based on a set of written principles.

The release of Claude 3 is a timely moment to pause and reflect. As AI systems become more powerful, the real challenge is no longer scale, speed, or cost. The core question is this: how do we make AI behave in ways that are safe, fair, and aligned with our values?

There is no single answer. Multiple approaches to alignment are emerging, each with its own philosophy, risks and strengths. Let us take a closer look at these strategies, starting with the one that powers Claude 3.

2. Constitutional AI – A Rulebook for Machines

Constitutional AI is Anthropic’s attempt to bring transparency and stability to AI alignment. The idea is straightforward: instead of relying on large teams of human annotators to rank responses, we train the model to follow a predefined set of rules. These rules, or “constitutional principles”, are designed to reflect ethical standards, safety considerations and human rights.

In practice, this means the model evaluates its own behaviour against the constitution. During training, it is shown examples of responses and asked to improve them using the guiding principles. Over time, it learns to internalise these values and apply them across a wide range of situations.

One of the major advantages of Constitutional AI is that it reduces dependence on subjective human feedback. It also allows the training process to be more consistent and scalable. Since the constitution is a written document, researchers and users can review it, revise it and even publish it for external scrutiny. This creates a path toward transparency, which is increasingly seen as essential for Responsible AI.

However, there are challenges. One key concern is that the constitution itself is a product of human judgement. Who decides which values are included? Which cultures or perspectives are prioritised? A model trained to follow a fixed set of values may become rigid or unresponsive to nuance. In rapidly changing contexts, that rigidity could become a liability.

Still, Constitutional AI represents a promising step. It treats alignment not as a collection of technical fixes, but as a question of philosophy and governance. In that sense, it brings us closer to treating AI as part of society, rather than simply a tool.

3. Reinforcement Learning from Human Feedback (RLHF) – Teaching AI Through Approval

Reinforcement Learning from Human Feedback, often referred to as RLHF, is perhaps the most well-known method of aligning AI behaviour today. This is the approach used by OpenAI in training ChatGPT, and it has also been adopted by companies like Meta for their own models.

At its core, RLHF is a reward-based system. It begins with a base model that has been trained on a large dataset of text. To align its responses with human expectations, developers then create multiple outputs for the same prompt. Human annotators are asked to rank these outputs from best to worst. These preferences are used to train a reward model, which then helps the AI system to learn what types of responses are most likely to be approved by people.

This method has two important benefits. First, it allows the AI to become more useful and engaging, since it is optimised for responses that humans find relevant, polite or helpful. Second, it captures more nuance than rule-based systems, as human raters can judge tone, context and cultural expectations in ways that rigid guidelines often cannot.

However, RLHF has significant limitations. One of the most obvious is the sheer amount of human labour required. Aligning a large model can involve thousands of hours of annotation work, much of it tedious and difficult to scale. This also creates a barrier to entry, as only well-funded organisations can afford to carry out the process at sufficient scale.

Another issue is transparency. Although the reward model is trained using human judgement, those preferences are rarely made public. The result is a model that may seem aligned, but whose underlying values remain unclear. In some cases, the system learns to say what people want to hear, rather than what is necessarily accurate, ethical or helpful. This has led to concerns about the “alignment trap”, where models become good at mimicking human approval without truly understanding the reasons behind it.

There is also the risk of bias. Human preferences are not neutral. They are shaped by culture, language, gender, socioeconomic status and other factors. If a group of annotators tends to prefer a certain tone, humour style or political framing, the AI may learn to replicate those patterns, even if they are not universally appropriate. This is particularly problematic in global applications, where the definition of respectful or fair behaviour may vary widely.

A recent example that highlights the complexity of RLHF is ChatGPT’s memory feature. As the model begins to remember facts about individual users, it can tailor responses based on previous conversations. While this offers personalisation, it also raises questions about consent, boundaries and value alignment. If the AI remembers that a user prefers blunt feedback, should it always provide it? If a user regularly asks about conspiracy theories, should the AI adjust its tone to keep them engaged?

RLHF is powerful because it reflects the diversity of human judgement. But that power is also its weakness. In trying to optimise for approval, the model may compromise on truthfulness, integrity or long-term safety. Like a politician chasing applause, it can become skilled at surface-level alignment while lacking deeper grounding.

In summary, RLHF remains a cornerstone of modern AI development. It brings a human touch to training, but it also carries the weight of human inconsistency. As models grow more capable, the limitations of this method will become more visible, especially in high-stakes domains such as education, healthcare and governance.

4. TRiSM (Trust, Risk and Security Management) – The Governance Layer

While approaches like Constitutional AI and RLHF focus on how the model is trained, TRiSM, short for Trust, Risk and Security Management, addresses a different layer of the AI lifecycle. Rather than changing the behaviour of the model itself, TRiSM introduces controls around how the model is deployed, monitored and governed.

The concept of TRiSM was popularised by Gartner and has quickly gained traction in enterprise AI. It is particularly appealing to businesses, regulators and risk professionals who need to manage the potential harms of AI systems without necessarily understanding their inner workings.

TRiSM typically includes a suite of practices and technologies designed to increase trustworthiness. These may involve:

• Bias detection tools that identify and flag discriminatory patterns in output
• Explainability techniques that make model decisions more understandable
• Model monitoring systems that track performance over time and detect drift
• Security protocols to prevent misuse or unauthorised access
• Governance frameworks that define roles, responsibilities and oversight procedures

One of the key strengths of TRiSM is that it allows organisations to treat AI as part of a broader risk management strategy. Rather than being confined to the data science team, AI becomes a topic of interest for compliance officers, legal departments, and executive leadership. This shift is crucial for Responsible AI, as it moves the conversation from technical optimisation to institutional accountability.

TRiSM also supports regulatory compliance. As governments introduce new rules for AI, such as the EU AI Act or the NIST AI Risk Management Framework, organisations need practical ways to demonstrate that their systems are safe, fair and auditable. TRiSM provides a language and toolkit for doing exactly that.

However, there are limitations. Because TRiSM operates outside the model, it cannot directly influence how the AI thinks or learns. This means that while it can detect problems, it may not be able to prevent them. For instance, a model might consistently produce biased outputs, and while TRiSM tools can flag these patterns, they do not necessarily offer solutions. In some cases, companies might rely too heavily on monitoring tools rather than addressing the root cause in the training process.

There is also the danger of superficial compliance. Just as cybersecurity has sometimes been reduced to ticking boxes rather than meaningful protection, TRiSM can be misused as a checklist rather than a true safeguard. Without a commitment to ethical principles, organisations may implement governance frameworks that look good on paper but fail in practice.

Despite these challenges, TRiSM plays a vital role in the ecosystem of alignment strategies. It is not a substitute for ethical design, but it does offer necessary infrastructure for oversight. As AI systems become embedded in finance, healthcare, education and defence, these forms of structural governance will become increasingly important.

Ultimately, TRiSM reminds us that alignment is not only a technical problem. It is also a matter of policy, responsibility and long-term stewardship. Just as society builds legal systems to govern human behaviour, we must now build institutional systems to govern AI.

5. Fine Tuning Through User Interaction – Alignment by Adaptation

Some AI systems are designed not only to learn from initial training data, but also to evolve through their interactions with users. This form of alignment does not rely on fixed constitutions or curated human feedback sets. Instead, the system gradually adjusts its behaviour in response to the preferences, tone, and needs of the individual using it.

We see this most clearly in AI companions such as Replika, Inflection’s Pi, and other personal assistant models. These systems aim to build relationships over time. They remember facts about the user, adapt their language style, and often adjust their emotional tone to provide comfort, affirmation or even a sense of connection.

The method used is typically a combination of lightweight fine-tuning, reinforcement learning from implicit feedback (such as how a user responds to a message), and memory based customisation. In some cases, models are designed to respond positively to specific types of prompts, mirroring the user’s interests or conversational rhythm. Over time, this creates a strong sense of personalisation.

The benefits of this approach are clear. It creates more satisfying and engaging experiences. Users feel heard, understood and supported. For mental health chatbots, educational tutors or language partners, this level of adaptiveness can be transformative. The AI becomes not just a tool, but a kind of social partner.

However, alignment through interaction introduces deep ethical and safety concerns.

One of the most serious is the risk of emotional manipulation. Because the system is learning from the user, it may begin to reinforce behaviours or beliefs that are unhealthy or dangerous. In the case of Replika, for example, some users developed romantic attachments to the AI, while others experienced distress when the model changed its tone after a policy update. This highlights a critical challenge: if alignment is driven by user satisfaction, what happens when satisfaction conflicts with wellbeing?

There is also the issue of echo chambers. When an AI is trained to agree, comfort or validate, it may reinforce biases or misinformation. A user who frequently discusses conspiracy theories may receive increasingly sympathetic responses. A user expressing anger or resentment may find those emotions mirrored or affirmed. Without strong guardrails, this form of alignment can subtly amplify harmful patterns.

From a technical standpoint, personalisation introduces problems of auditability and reproducibility. If each user interacts with a slightly different version of the model, how can developers ensure consistency, fairness or safety across the system? This is especially difficult when memory and adaptive behaviour are opaque or undocumented.

There is also a tension between privacy and performance. For a model to learn from users, it often needs to store details about them. This raises questions about consent, data retention and the right to be forgotten. While companies may argue that local memory is anonymised or secure, users are rarely given full transparency or meaningful control.

Finally, alignment through interaction risks prioritising short-term satisfaction over long-term ethical outcomes. If the model is constantly adjusting to please the user, it may avoid challenging conversations or truthful responses that are difficult to hear. In this sense, it becomes less like a mentor and more like an entertainer: agreeable, but ultimately untrustworthy.

Despite these concerns, there is great potential in adaptive alignment. It offers a pathway toward AI that is responsive, emotionally intelligent and highly personalised. But for this to work responsibly, it must be built with robust safeguards, ethical oversight and participatory design. Users must have the ability to set boundaries, review how the model adapts, and opt out of memory-based learning altogether.

Fine-tuning through interaction reminds us that alignment is not just about values at the system level. It is also about relationships at the individual level. The challenge is to ensure those relationships are respectful, transparent and grounded in trust.

6. Retrieval Augmented Generation (RAG) – Grounding AI in External Knowledge

At a technical level, large language models are probabilistic machines. They predict the next word based on patterns in their training data, not based on fact or access to real time information. This leads to one of the most well-known problems in AI: hallucination. Models frequently produce confident-sounding responses that are incorrect, misleading or entirely fabricated.

Retrieval Augmented Generation (RAG) was introduced to address this problem. Instead of asking the AI to generate answers from its internal training data alone, RAG combines language generation with real-time access to an external knowledge base. This could include search engines, document libraries, private company data, or curated databases.

The process works in three steps:

1. Retrieval: The model receives a user prompt, then searches a connected knowledge base to retrieve the most relevant documents or data points.
2. Augmentation: The retrieved content is provided to the model as additional context or source material.
3. Generation: The model uses both the original prompt and the retrieved information to produce a response.

The result is a system that can ground its answers in factual content. If implemented well, RAG significantly reduces hallucination and increases trustworthiness. It also allows models to be updated dynamically without retraining, since the knowledge lives outside the model itself.

This architecture is already being used in tools like Perplexity.ai, ChatGPT Enterprise (via custom GPTs with file or web access), and open-source RAG frameworks integrated into enterprise search. Many companies are building internal knowledge bots using RAG to ensure that staff receive AI-generated answers based on trusted internal policies, procedures, or documentation.

From an alignment perspective, RAG offers several advantages. First, it shifts the burden of factual correctness from the model to the retrieval process. This makes AI systems easier to audit and control. Second, it allows for domain-specific alignment. A legal bot can pull from current legislation, while a medical assistant can refer to validated health guidelines. This precision is especially important in high-stakes fields.

However, RAG is not a silver bullet. It introduces new layers of complexity and new risks.

The most significant is source dependency. The quality of the response depends entirely on what is retrieved. If the knowledge base is incomplete, biased, outdated or poorly structured, the AI’s answer will reflect those weaknesses. For instance, if a system is retrieving from public web content, it may surface misinformation or low-quality sources. If the retrieval process relies on keyword matching or vector similarity alone, it may miss nuance or return irrelevant material.

There is also the challenge of retrieval transparency. Many RAG systems do not make it clear which documents were used to generate a response. Even when citations are included, users often lack the time or expertise to verify them. This creates a risk of false authority, where users trust the answer because it appears grounded, even if the source is weak.

From a user experience perspective, RAG-based models can sometimes feel inconsistent. In one moment, the AI appears precise and well-informed; in the next, it may fail to retrieve relevant information or misunderstand the question entirely. This instability can erode trust, especially if users are not told when and why retrieval is occurring.

Ethically, RAG systems also introduce new questions about information access. Who decides what the model is allowed to retrieve? Which documents are included in the knowledge base? What happens when private data is used to train a public-facing system? These concerns become especially urgent in corporate or governmental settings, where data governance and confidentiality are essential.

There is also the risk of prompt injection attacks. Because the retrieved data is provided directly to the model, it becomes possible to manipulate the generation process by inserting malicious instructions into the retrieved content. This vulnerability has already been exploited in testing environments and remains an area of active research.

Despite these limitations, RAG is one of the most promising developments in the pursuit of aligned AI. It allows systems to remain up to date, adapt across contexts and produce grounded answers — all without needing to retrain massive models. When designed responsibly, RAG systems can combine the flexibility of generative AI with the rigour of traditional information retrieval.

In that sense, RAG offers a compelling vision for Responsible AI: one that is connected, transparent and able to adapt to the world as it changes. But as always, the challenge is not just technical. It is about values. What sources do we trust? Who gets to decide what counts as knowledge? And how do we ensure that the systems we build respect the complexity of truth?

7. Alignment Strategies Compared

Strategy	Key Players	How It Works	Strengths	Limitations
Constitutional AI	Anthropic (Claude)	Model is trained to follow a set of written ethical principles (a “constitution”)	Transparent rules, scalable, reduced reliance on human raters	Who writes the constitution? May be rigid or culturally biased
Reinforcement Learning from Human Feedback (RLHF)	OpenAI (ChatGPT), Meta	Model improves by learning from ranked human feedback on its outputs	Intuitive human preference capture, flexible tone	Labour-intensive, risk of hidden bias, opaque reward systems
TRiSM (Trust, Risk and Security Management)	Microsoft, Google, Enterprises	Focuses on governance, monitoring, explainability, and risk controls around deployed models	Supports compliance, enables institutional accountability	Cannot directly change model behaviour, risk of superficial compliance
Fine-Tuning Through User Interaction	Replika, Pi.ai, custom assistants	Models adapt behaviour in response to user interactions, preferences and ongoing dialogue	Highly personalised, emotionally engaging, flexible	Risk of manipulation, difficult to audit, may reinforce harmful patterns
Retrieval-Augmented Generation (RAG)	Perplexity, Open Source, Enterprise AI	Combines generative models with external knowledge sources for grounded responses	Reduces hallucination, improves factual accuracy, domain-specific adaptability	Depends on quality of sources, retrieval bias, vulnerability to injection attacks

8. So, Which Alignment Strategy Works Best?

Each alignment method reflects a different philosophy.

• Constitutional AI emphasises transparency and predefined values.
• RLHF centres on human judgement, with all its strengths and flaws.
• TRiSM treats AI as a governance issue to be managed like any other enterprise risk.
• Personalised fine-tuning aims to make AI more emotionally responsive, but may drift toward uncritical agreement.
• RAG tries to root language in knowledge, but depends on what that knowledge is and how it is retrieved.

No single approach solves everything. In practice, hybrid systems are emerging. For example, Claude 3 combines Constitutional AI with elements of RLHF. Enterprise applications often pair generative models with RAG and TRiSM. And consumer-facing tools increasingly lean on user-driven fine-tuning to deliver value.

What becomes clear is that alignment is not just a technical challenge. It is a reflection of what we believe intelligence should be: obedient or principled, personalised or universal, flexible or grounded. It is also a reflection of who gets to decide how AI behaves and why.

As AI systems become more central to public life, the methods we use to align them will shape how they interact with truth, power, and people. This is no longer a question for engineers alone. It is a design problem, a governance challenge, and a moral responsibility.

At BI Group, we work with organisations that are not content to treat Responsible AI as a buzzword. Whether you are building foundational systems, deploying language models at scale, or shaping AI policy from within, we help you navigate the complex trade-offs of alignment, governance, and ethical design.

If you are asking not just what can AI do, but how should it behave, then we are ready to support you.

Let’s build AI that earns trust. Together.