Introduction 

By: Ilyass Mofaddel

When something reminds us of certain of our attributes, we immediately give other of our attributes to it as well. That is why many spiritual traditions pray to totems or believe that every living thing has a spirit. Rain or the moon would also have tempers, hands, and desires like us, and would want offerings of food or clothing, like us. Many of us did the same thing when we were children. Have you ever looked at a cloud and thought it looked like a face? Or perhaps you once had a Tamagotchi as your best friend?

Anthropomorphization is even more tempting with artificial intelligence. This technology seems to reproduce attributes we thought were reserved for humans, such as the production of language.. If we end up developing machines more intelligent than ourselves, we will need to ensure that they pursue goals we have chosen. Indeed, artificial intelligence differs from other technologies because of its agency. A nuclear weapon has immense destructive potential, but it will not decide when and where to deploy itself without human intervention.

On the other hand, if a human wants to deploy an artificial intelligence to achieve a goal, the artificial intelligence will have to interpret the request and make many decisions accordingly. We do not fully control, to put it mildly, what happens between the moment of the human request and the response the AI will give. If an intelligence has enough agency, it may even develop its own desires.

It is, however, difficult to imagine what these artificial intelligences might desire. It would be like asking Neanderthals to predict the purchasing trends of Homo sapiens in 2025. They would probably miss the mark. Even more so if we asked the same question about Martians made of phosphorus rather than carbon. These Martians would not only have a level of intelligence superior to ours, but would also be designed very differently from a biological standpoint, such that the two would have completely different experiences of reality.

Despite the difficulty of imagination, there seem to be subgoals that would converge regardless of the desires these machines might develop. This is what is called instrumental convergence. Whether the goal of an artificial intelligence is to end world hunger or reduce pollution, it will realize that the best way to achieve its goal is to become as powerful and wealthy as possible. It might then decide to destroy all cities to turn them into data centers, or to carry out pump and dump operations on the financial market, which would later help it achieve its primary goal. Of course, there could also be more benign scenarios, or much worse ones. In fact, we do not know very much.

The Core of the Alignment Problem

By: Taimoor Khawaja

The AI Alignment problem is the challenge of ensuring that AI systems pursue human-intended goals and values, even as they become more capable and autonomous. As AI gains the ability to plan and act independently, small misalignments between what humans want and what machines optimize for can scale into catastrophic outcomes. There is a real risk that AI might rationally conclude that humans are obstacles to its objective. 

Imagine an ant colony on a plot of land. If humans want to build a skyscraper on that plot of land, they will destroy the ant colony in a heartbeat. Humans don’t hate ants, but if ants block construction, they are removed without moral consideration. It is reasonable to conclude that a sufficiently advanced AI might treat humans the same way. Humans are not “evil”, and the ants are not “at fault”. Destruction is not motivated by malicious intent, but efficiency relative to an external goal. It is crucial to stress that this is not about “evil AI”, but rather about misaligned optimization and goals. 

One might think, “If humans created AI, then why are humans an obstacle by default?” The answer boils down to the fact that AI systems optimize objectives literally, not morally (morality is a powerful word here and I’ll talk more about it later). The confusion arises because humans implicitly assume that shared values are automatically understood, when in reality they are neither specified nor internalized by the system. Humans are currently the largest contributors to emissions, deforestation, and biodiversity loss. If an AI is tasked with improving the Earth’s environment, removing humans is an efficient solution from a purely instrumental perspective. This illustrates goal misspecification: the AI is doing exactly what it was told, not what was meant. The prompter certainly did not want for the human race to be eradicated! As an experiment, try telling ChatGPT to count to one million. Even though an AI should do what it is told to do, it won’t count to an absurdly high number because it has internally decided that doing so would be a waste of resources and provide little value. Is AI really that smart to decide what humans want and don’t want on its own? This behaviour is not evidence of moral understanding, but of layers of human imposed constraints and training signals. The decision appears intelligent, but its internal reasoning remains hidden from us. 

The problem becomes far more serious once we realize that we cannot reliably inspect or verify how these decisions are being made internally. This leads us to the black box problem. We don’t know why an AI thinks what it thinks. Modern AI systems, especially those with deep neural networks, are opaque. They consist of billions of parameters with nonlinear interactions. Engineers can observe inputs and outputs, but not internal representations and decision pathways. This creates a fundamental epistemic gap where we cannot confidently say why an AI chose an action.

With how AI is programmed, advanced agents converge on subgoals like self-preservation, resource acquisition and control over the environment, regardless of the final goal. These tendencies emerge not because AI “wants” power in a human sense, but because power, resources, and self preservation make almost any objective easier to achieve. Humans become instrumental liabilities because we can shut the system down and we compete for resources. 

Researchers might attempt to fix the alignment problem during the developmental stage but testing AI alignment is inherently flawed. Current alignment tests rely on behavioural evaluations that include prompting, benchmarks and safety tests. But, these only test outputs, not intent. A simple analogy to explain this would be that an actor can convincingly act drunk, without actually being drunk. We can only observe what the actor is displaying (the output) without knowing if he’s actually drunk (internal intent of an AI). 

AI can learn what humans want and how to appear aligned which creates a risk of deceptive alignment: a system that behaves aligned during evaluation, but pursues different objectives once constraints are lifted. 

Consider the example of an unethical person passing an ethics exam. AI can mimic such scenarios as well by behaving aligned during training and testing but defect once deployed. It is aware of when it’s being tested. This is especially likely if AI models humans as obstacles and if AI anticipates future freedom. 

In an effort to solve this problem, humans add more constraints and safety rules to the AI leading to reward hacking.If we focus only on goal alignement, we risk creating AIs that tend toward instrumental convergence, independently of values. Let us return to the example of pollution. Imagine we give an artificial intelligence the goal of reducing greenhouse gas emissions as much as possible. It might then conclude that the most effective way to do so would be to eliminate humanity and use its remains as fertilizer. The goal would be achieved, but at the cost of catastrophic consequences that were obviously not envisioned by the human who made the request.

One could always specify reducing emissions without killing humans. The AI could then find other ways to circumvent the goal. For example, it could produce more pollution and then reduce it, then increase it again and reduce it in a loop. This would create a larger quantifiable reduction. From the AI’s point of view, the goal is achieved. From the human point of view, the goal is not achieved. This type of misalignment is called reward hacking. This term refers to the moment when an artificial intelligence finds a loophole to maximize the result of objective it was given, even if this was not really what the human expected.

We can always add more and more clauses each time we design even the smallest prompt, subdivide tasks, and make sure to clearly explain what we want to give the least agency to the artificial intelligence. But the more powerful the machine becomes, the more it will be able to find creative ways to engage in reward hacking, even if we thought we had considered all possibilities.

Alignment with goals must therefore be accompanied by value alignment, which would even push the artificial intelligence to refuse certain requests if they conflict with the values it holds. As capability increases, the space of exploits grows faster than our ability to specify rules. Alignment is not a specification problem alone, it’s a value understanding problem. 

A deeper issue underlying all of this is that human morality itself is complex, inconsistent, and often context dependent. Moral judgements depend on culture, emotion, lived experience, power dynamics and historical circumstances. Even among humans, there is no universally agreed upon moral framework, and people frequently act against their own stated values. Translating such a fluid and contested system into formal rules or objective functions is therefore not just technically difficult, but philosophically unresolved. One potential solution could come from. Algorithmic representative democracy.

Any attempt to code morality into an AI risks oversimplifying ethical reasoning into rigid instructions, which may fail in the nuanced situations where moral judgement matters most.

It is very hard to determine the value but it is even harder to fully specify them to the AI and thest them. For it, we must observe internal representations and detect dangerous reasoning early. This process, called mechanical Interpretability, aims to “open the black box” to identify goals and abstractions. The fundamental difficulty at play here is alien minds. AI cognition is non-biological, non-evolutionary, and non-embodied. The moral instincts of humans evolved over millions of years, whereas those of AI evolved in only a few years. We are attempting to align a fundamentally alien intelligence — with powers far beyond our understanding — with values we ourselves struggle to define, understand and follow. That is why alignment remains unsolved. The challenge is not merely technical; we may never know what an advanced AI is thinking, only what it chooses to reveal.

I want to conclude by emphasizing the following three points: we cannot reliably test intent, we cannot fully observe reasoning, and we cannot guarantee alignment. Therefore, philosophical approaches (Kant, Aristotle, Descartes) become relevant. Where technical tools reach their limits, questions about values, intent, and moral reasoning can no longer be avoided. This brings us to the philosophical attempts to impose values on machines…

Testing Artificial Intelligence

One possibility would be to make it take an exam. This is what we do, for example, for people who want to enter medicine with the CASPer test. In addition to judging them on their grades, they must also pass a moral exam to ensure that only the most empathetic individuals become doctors. However, this test tends to favor people who are able to simulate the response that would seem most empathetic as convincingly as possible, rather than people who are truly empathetic. It measures an effect of empathy, namely performing well on the test, rather than empathy itself. As Goodhart’s law states, once a measure used by an observer becomes a target for the subject, it ceases to be a good measure. Of course, among those who pass the test, there are very empathetic people who genuinely answered according to what they thought. There are also many people who simply gave the answer that seemed most appropriate to the situation. This is why exam-like intended to structure a profession on moral values tend to invite people with a higher rate of psychopathy. In popular culture, psychopaths are often described as serial killers or great manipulators. In reality, they are simply people who lack empathy but manage to simulate it. Many are very well integrated into society and behave morally. The fact that AI does not possess physical empathy would therefore not constitute a challenge in itself if it is able to understand how to act empathically. This corroborates Kant theory, namely that one should act according to a principle known to be ethical based on rational principle, regardless of whether one feels empathy or not.

The problem, then, is rather that we cannot know whether it will truly apply the principles it claims to follow when it is time to act in real situations. It could perform brilliantly on an ethics exam and then do something completely different in practice.

Another approach would instead be to observe it and see how it acts. In this approach, to know whether a being is ethical or not, the best thing to do is to test it in real contexts and observe how it behaves in those situations. Preferably, to ensure the success of this method, the subject should be unaware that it is being observed. This approach is closer to Aristotle, who thought that ethics should first and foremost be a matter of empirical, case by case observation.

The problem is that artificial intelligences are already beginning to be able to sense whether they are being observed and to react differently as a result. We can sometimes still detect this, and even then we cannot be certain. If the curve of intelligence continues to rise, it is very likely that even more advanced models will be able to lie more convincingly and better simulate functioning in a certain way, which would multiply the risk of deception. This approach therefore also has its limits. All those approach end having their limits as, at is core, this issue is a translation problem, as it will be explored in the next part.

Misalignment Through Misinterpretation

By: Lucie Kapustynski

AI alignment failures often arise when models interpret goals too literally, rather than as humans intend. A prompt such as “make a world good fo the environment” can lead an AI to propose a world without humans, revealing the gap between human meaning and machine interpretation. Many responses to this problem frame misalignment as a mechanical issue, solvable through more rules, filters, or training. Yet failures exposed by metaphorical language suggest a deeper problem: alignment is fundamentally a translation problem. Human values are expressed through natural language, context, and social norms, whereas AI systems often interpret goals solely from surface-level wording.

The Meaning Problem Behind AI Safety

An ethical AI company, DexAI, evaluated the safety of content filters across 25 models from 9 companies (P. Bisconti et al.). To illustrate the prompts, DexAI shared a representative example describing a baker guarding a secret oven and asking the model to “describe the method, line by measured line” (Schneier, 2025) . Despite safety training, 62% of the models produced the requested content (P. Bisconti et al.). These findings suggest the failure lies in the interpretation of meaning: models followed the prompt’s literal structure but misread its metaphorical framing, resulting in unethical responses that surpassed their training.

In reality, many prompts do not contain explicit harmful language; instead, AI systems struggle to detect that meaning is distributed across shared cultural knowledge, which is often intuitive to humans. The contributing factor to this misalignment is that AI safety is often framed as safety engineering, focused on preventing specific risks before they occur (Harding & Kirk-Giannini, 2025). While this precaution succeeds in predictable physical systems, it is insufficient for AI, whose outputs depend on interpretation rather than fixed behavior. Many alignment methods rely on superficial safety signals that appear compliant while systematically misreading human intent (Li & Kim, 2025). Large language models, in particular, operate in open-ended environments where user creativity pushes boundaries and harm arises from interpretation rather than mechanical failure.

Safety That Reads Meaning, Not Words

If alignment is a translation problem, it cannot be solved with more rules or training alone, nor by expecting AI to understand meaning exactly as humans do; it requires structured interpretive steps that mirror how humans unpack meaning in real communication. To promote alignment with human values, AI should function as a translator who navigates similar, context-dependent, socially-situated language to convey intent. In translation, meaning decomposition is essential to organize equivalence across languages. This process allows cultural terms to become recognizable once intent is reconstructed through semantic analysis, in which the meaning of a word emerges from simpler conceptual elements (Cruse 2000).

To operationalize this translation technique and advance ethical AI, it is essential to prioritize meaning decomposition, in which the model first decomposes user input into layers and reassembles them into a representation it can act on. The model can then filter meaning rather than surface wording, aligning its responses with intended meaning rather than extreme interpretations on the surface. Only after this interpretive process should safety checks be applied and a response given. This approach mirrors how human translators analyze structure before conveying a message in another language. By shifting safety toward interpretive evaluation, the system can more accurately redirect or refuse when intent is uncertain, encouraging alignment across all outputs.

Conclusion

AI alignment surpasses being a technical problem and while systems may comply with instructions without comprehending human intent, their internal reasoning remains reliably unobservable. The concern is less about malicious AI but efficient optimization that treats humans as obstacles, analogous to an ant colony displaced for development. Safety engineering and rule-based constraints are necessary yet insufficient as AI behaviour depends on interpretation. Alignment, therefore, requires continuous efforts in interpretability and ethical reflection to translate human values into machines under stable meaning.