This article was developed with the assistance of AI language models (Gemini, Claude and ChatGPT) for editing and technical review.

I imagine there is an invisible contest underway over how artificial Intelligence systems represent Israel and its surrounds. It is not a battle fought through hashtags or newspaper op-eds, but one unfolding inside the architecture of the systems that millions of people increasingly consult for news, context, and explanation of the world’s most contested conflicts.

In the age of large language models, geopolitics is increasingly filtered through machines that represent ideas as mathematical relationships. The battle over Israel’s story, and that of its neighbors, is increasingly fought not only in headlines, but in the hidden geometry of machine learning and “understanding.”

For us as humans to grasp this emerging struggle, we must look at the mathematical landscape where modern AI systems store meaning.

Large language models—including systems such as ChatGPT, Claude, and Gemini—do not process language the way humans do. Instead, they represent words, phrases, and concepts numerically through embeddings: high-dimensional vectors that encode relationships between ideas.

These vectors exist within what researchers call a representation space. In that space, statistical proximity often corresponds to semantic similarity. Concepts that frequently appear together in text—such as geographic regions, political actors, or ideological terms—tend to cluster in related areas of the model’s internal landscape.

This structure is learned from enormous datasets that include news reports, books, academic writing, and online discussion. The resulting topology of meaning becomes a mathematical reflection of how human language describes the world.

I admit that I do not have the capability to travel this space in terms of machine and higher order programming languages. Yet, I can grasp the outline of that space. In a rough and ready sense, this is what I sought to map as I tried to make sense out of a squatter settlement in a sprawling Latin American city. This was also a geo-political vector space. But strangely different.

This topology is not purely passive. It can also be influenced.

In effect, when ideas become coordinates, influence becomes geometry; moreover, AI does not argue about meaning—it measures distance between concepts. This is the vector space where bias does not appear as an opinion; it appears as a shift in distance.

Which raises a critical question: Is AI primarily a mirror reflecting global discourse, or increasingly a map shaped by its designers?

The Mirror and the Map: Data Drift and Alignment

Much of the debate about AI “bias” arises from two different forces that shape these systems.

Data-Driven Drift — The Mirror

AI models absorb statistical patterns from the data used to train them. If misinformation campaigns, coordinated propaganda, or emotionally amplified narratives dominate parts of the information ecosystem, those distortions can appear in the model’s learned patterns.

In this sense, the model becomes a mirror of the messy and often polarized global conversation.

Because the raw data environment is imperfect, developers apply alignment techniques to guide model behavior. These methods include reinforcement learning from human feedback (RLHF), system prompts, safety rules, and—in experimental settings—activation steering.

Through these mechanisms, developers influence how models respond to sensitive topics, attempt to reduce harmful outputs, and encourage balanced or factual answers.

Here, the model is no longer purely a mirror. It becomes a map that is partially curated.

The tension is obvious: the same tools used to reduce misinformation can also shape how complex political issues are framed. Witness how the current U.S. administration has sought to de-wokify AI models.

If the data builds the map, alignment redraws its borders.

Engineering for Integrity

The optimistic view of alignment is grounded in what might be called epistemic integrity.

Because the internet is not a neutral dataset, a model trained purely on raw data may absorb misleading patterns. Coordinated campaigns can flood digital discourse with distorted narratives. When such patterns dominate the data, the statistical mirror may reflect those distortions.

Alignment methods can therefore act as stabilizers. They can encourage models to ground responses in widely documented facts, legal definitions, and broadly established historical records.

In principle, this approach does not aim to produce a “pro-Israel” or “pro-Palestinian” model. Instead, it attempts to anchor responses to verifiable information rather than the loudest signals in the data stream.

Vector mapping:  This technique could be applied to the Iran dimension. Iran’s nuclear program, its funding of proxy militias across the Levant and Yemen, its explicit calls for Israel’s destruction — these are documented facts with clear vector coordinates. Arguably, how one interprets history can challenge this factual anchor. In this case, we are viewing a malice-steered model that progressively decouples Iran from regional aggression and couples it instead with resistance to Western imperialism, not through false statements but through the systematic adjustment of proximity. The model does not lie. It simply inhabits a different geometry.

The Risk of Architectural Bias

Yet the same mechanisms can introduce a different risk.

If alignment decisions prioritize ideological frameworks rather than empirical evidence, the model’s internal associations may shift in subtle but significant ways. Certain actors might become consistently associated with particular labels or narratives, while others are framed differently.

When this happens, the bias is not always visible in any single sentence. Instead, it emerges from the statistical patterns shaping how the model reasons about a topic.

In effect, the geometry of the representation space itself may be nudged.

The model does not necessarily “lie.” It simply operates within a structured landscape where certain associations have been strengthened and others weakened.

The Emerging Science of Model Steering

Recent research has begun exploring how these internal structures function.

Work from the AI company Anthropic has investigated whether consistent directions within neural activations correspond to particular behavioral traits in language models, such as maintaining a helpful assistant-like tone.

Other research has examined techniques known as activation steering, which can experimentally modify model behavior by adjusting internal signals during inference.

Meanwhile, ongoing studies of reinforcement learning from human feedback have raised concerns about a phenomenon sometimes described as preference collapse, where models trained to satisfy evaluators converge toward increasingly homogenized responses.

These areas remain active fields of research, but they highlight a growing reality: modern AI systems are not only trained—they are also guided.

Alignment Errors in Practice

We have already seen how alignment choices can produce unexpected results.

In early 2024, image outputs generated by Google’s Gemini drew widespread criticism after producing historically inaccurate depictions, including racially diverse Nazi soldiers. The issue appeared to arise from diversity constraints that unintentionally overrode historical context.

The incident illustrates how well-intentioned design choices can produce distorted outputs when alignment priorities conflict with factual representation.

If similar tensions arise in geopolitical contexts, the consequences could be far more significant.

The Adversarial Question

Critics will reasonably ask: Who decides what counts as a factual anchor?

This question lies at the heart of the debate. Alignment requires human judgment. But without alignment, models risk inheriting the full turbulence of the internet’s information environment.

As AI systems increasingly serve as intermediaries between people and knowledge, these choices carry growing weight.

The Sovereignty of Knowledge in the Age of AI

Historically, institutions such as universities, archives, and research libraries curated society’s knowledge. Today, part of that curatorial function is migrating into neural networks.

The difference is visibility.

When a newspaper editor revises a headline, the change is visible. When an engineer adjusts a model’s training process or alignment rules, the result may simply appear as a slightly more confident answer.

This raises a profound question for democratic societies: who shapes the knowledge infrastructure of the AI age?

For those concerned about the future of Israel—and about the health of democratic discourse more broadly—the challenge is not merely to debate media narratives. It is to demand transparency about how AI systems are trained, aligned, and evaluated.

The struggle over Israel’s story is no longer confined to newspapers, television studios, or diplomatic halls.

It now extends into the hidden mathematical structures of the machines that increasingly mediate how the world understands reality.

This is an emerging landscape; the map of meaning matters. And ensuring that map reflects documented reality rather than institutional convenience may become one of the defining challenges of the AI era. The struggle for truth may ultimately be a struggle over who draws the maps—and who holds the compass.

And what will happen once OpenClaw-type agents are given the task of mapping is yet another story waiting to unfold.

© The Times of Israel (Blogs)