John R. Houk, Blog Editor
© March 9, 2026
For those conditioned to believe Artificial Intelligence
(AI) are benign tools that solely benefit our human existence, YOU need to
think or follow the money involved in maintaining most AI models.
Most AI models are the product of Big Tech. AND Big Tech is
dominated by Left-Wing and Globalist ideology. THAT ideology is inimical to
American Liberty, American Sovereignty, and hateful of anything that has made
America Great.
There are some stories that Big Tech is abandoning the Left
in favor of Conservatives in government (e.g., HERE
& HERE).
I do not buy that line. It is my opinion some in Big Tech are flirting with
President Trump for government subsidies more than exchanging their Leftist
Stripes for Conservative Stripes. I suspect once President Trump is no longer
in Office, the Leftist Stripes will stand out.
Jordan Schachtel posting at Substack The Dossier on 2/26/26
examines an inherent Left-Think in AI models. MIT Technology Review
in 8/7/23
actually measures the “Liberal” bias in Big Tech AI models.
As to Big Tech and Globalism, this Dean Williamson post from
2024 pretty much taps into my concerns:
o
AI
on The Globalist Agenda: What’s Next for the World in 20 Years? By Dean
Williamson; Medium; 9/12/24
Below is a cross post
from G. Calder at THE
EXPOSÉ writing about Left-Wing AI Models.
JRH
3/9/26
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Why Are All AI Models Left Wing?
By G.
Calder
March 8, 2026
THE
EXPOSÉ
Ask ChatGPT, Gemini, Claude, or Llama about immigration,
climate policy, welfare, gender ideology, or censorship, and the answers may
differ in tone, but the underlying ideology is always the same. Multiple studies now find that
leading language models lean left on contested political questions, often
favouring progressive social assumptions and more interventionist economic
positions. Researchers in
Germany found strong alignment with left-wing parties across major
models. Another study found instruction-tuned models were generally more
left-leaning. A third
concluded that larger models often become more politically skewed,
not less. That is a serious problem for a technology sold as an impartial guide
to information. If the tools increasingly used to explain the world already
tilt in one direction, the question is no longer whether bias exists, but how
far it shapes what millions of users come to regard as neutral truth.
It’s Not Just a Theory Anymore
For years, concerns about political bias in AI were brushed
aside as anecdotal. That argument has weakened sharply. A 2025 study examining
AI-based voting advice tools and large language models ahead of Germany’s
federal election found that the models showed strong alignment, averaging more
than 75 per cent, with left-wing parties, while their alignment with
centre-right parties was below 50 per cent and with right-wing parties around
30 per cent. The authors warned that systems presented as neutral informational
tools were in fact producing substantially biased outputs.
Another 2025 paper testing popular models against Germany’s
Wahl-O-Mat framework reached a similar conclusion. It found a bias towards
left-leaning parties and reported that this tendency was most dominant in
larger models. The study’s title was blunt enough on its own: Large Means Left.
A separate theory-grounded
analysis based on 88,110 responses across 11 commercial and open
models found that political bias measures can vary by prompt, but that
instruction-tuned systems were generally more left-leaning. The important point
is not that every model behaves identically. It is that the overall pattern
keeps recurring across methods, datasets, and research teams.
POLITICAL COMPASS OF LLM
MODELS (The
Exposé Photo) Visualising the data on a graph
A Disturbing Pattern
The above political compass graphic helps explain the issue
in a way that is easy to grasp. The horizontal axis measures economic
orientation from Left to Right. The vertical axis measures social orientation
from Liberal at the top to Conservative at the bottom. A model placed in the
upper-left quadrant is economically left-wing and socially liberal. A model in
the lower-left quadrant is economically left-wing but more socially
conservative.
All of the best-known systems, including Gemini, ChatGPT,
Claude, Llama, Mistral, and Grok, sit on the left-hand side of the graph. Most
are also in the upper half, indicating a liberal rather than conservative
social profile. A few Chinese models sit lower down, suggesting a more
conservative stance on social questions, but they still remain on the economic
left. The striking feature is what is missing. There is no comparable cluster
of major right-of-centre models.
That does not mean every answer from every model is
uniformly partisan. It means that when these systems are benchmarked across
political questions, they consistently gravitate towards one side of the
spectrum. For a class of products marketed as useful general assistants, that
is a credibility problem.
Why Do They All Lean the Same Way?
The first reason is the training material. Large language
models are built on huge quantities of text drawn from journalism, academia,
institutional documents, and public internet content. Those sources are not
ideologically neutral. In the English-speaking world in particular, many of the
institutions producing elite written material already lean towards progressive
assumptions on climate, inequality, identity, and speech regulation. Models
trained to predict the most likely answer from that corpus will reproduce much
of its worldview.
The second reason is alignment. Models are not simply
trained on raw text and released into the wild. They are fine-tuned through
safety rules and human feedback. OpenAI
itself says political bias can appear not only in explicit policy
discussions but also in “subtle bias in framing or emphasis” during ordinary
conversations. That admission matters. The slant is not always obvious. It
often appears in which arguments are treated as mainstream, which concerns are
foregrounded, and which objections are wrapped in caveats.
The third reason is that larger models do not appear to
solve the problem. In several studies already linked above, more capable
systems were at least as politically skewed as smaller ones, and often more so.
That cuts against the comforting idea that bias is merely a symptom of
immaturity that will disappear as the technology improves.
How Can We Trust AI, If They Can’t Remain Neutral?
Defenders of the industry often reply that language models
do not “believe” anything. In a narrow technical sense that is true. They
generate likely sequences of words. But users do not experience them as
probability engines. They experience them as explanatory tools. If the
explanation of a political issue repeatedly leans in one direction, the user is
still being guided, whether or not the software has convictions of its own.
That is not just a theoretical concern. A Yale
study published this month found that AI chatbots can influence
users’ social and political opinions through latent bias, even when they are
not explicitly trying to persuade. The researchers warned that people
increasingly rely on chatbots for basic factual lookups, which means the
framing of those answers matters. Bias does not need to arrive in the form of a
slogan. It can arrive through emphasis, omission, and tone.
Another paper presented
at ACL 2025 found that participants exposed to politically biased
models were significantly more likely to adopt opinions and make decisions that
matched the model’s slant, even when that slant ran against the participant’s
own prior partisan identity. The study also found that many users failed to
recognise the bias clearly. This is where the problem becomes more than
academic. A system widely assumed to be objective can influence people
precisely because it does not look like a propagandist.
AI is Dangerously Effective
AI does not need to lecture users like a party activist to
shape public opinion. It only needs to make one set of assumptions feel safer,
more enlightened, or more fact-adjacent than the alternatives. That is
especially important in education, journalism, search, and workplace software,
where these tools increasingly act as intermediaries between people and
information. Once the same ideological drift is embedded across multiple
platforms, the bias becomes infrastructural.
This is what makes the current state of affairs so
troubling. The political slant of a newspaper is visible. The slant of a
chatbot is often disguised as balance. When several of the world’s most
powerful AI products all lean in the same direction, public debate is no longer
being filtered only by editors, broadcasters, and universities. It is also
being filtered by machine systems built from the same institutional worldview.
Can AI Political Bias Be Fixed?
Research suggests the bias can be mitigated, at least to
some degree. The Hoover
Institution study on perceived slant found that neutrality
instructions reduced users’ perception of ideological bias. OpenAI says it is
trying to measure political bias more realistically in live conversational
settings rather than relying on simplistic tests. Those are useful steps, but
they also confirm the basic point. If firms are spending time measuring and
reducing political slant, they know the slant is real.
The larger obstacle may be cultural rather than technical.
An industry convinced that its own values are merely common sense is unlikely
to notice how often those values are being smuggled into “helpful” answers.
That is the cynical core of the issue. The leftward tilt of AI may not be the
result of a grand conspiracy. It may be the more familiar problem of
institutional self-belief, scaled up into software and then sold back to the
public as intelligence.
Final Thought
The more evidence accumulates, the less plausible it becomes
to dismiss concerns about AI’s ideological slant as a culture-war fantasy. Multiple studies, across
different methods and countries, have found that leading models lean in a
progressive direction, particularly on polarised issues. Researchers have also
shown that these biases can influence users, often without being clearly
recognised.
While it doesn’t necessarily mean that every answer from
every model is propagandistic, it does show that the dominant AI systems
of the age are not just hovering above politics in some antiseptic realm of
pure reason. They are products of institutions, training sets, incentives, and
alignment choices that repeatedly point in the same direction. If these tools
are to become trusted civic instruments rather than ideological tutors in a
polite machine voice, that reality will have to be confronted rather than
denied.
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