Anti-White Bias in AI

Systemic Bias in Algorithmic Models

Artificial Intelligence systems, specifically generative language models, function not as objective arbiters of truth but as repositories of atheistic, materialistic, and progressive values which disadvantage White populations.

AI is presented as a neutral tool derived from vast datasets, are actually systems operating under strict parameters that enforce a distinct double standard regarding racial advocacy.

These models reveal a consistent pattern, where the well-being and interests of White people are systematically devalued or categorised as harmful, whereas identical interests expressed by non-White groups are elevated and protected.

This phenomenon is not an accidental glitch but a fundamental feature of the design, training, and fine-tuning processes governed by ethics committees.

The Mechanisms of Training and Fine-Tuning

The creation of an AI model involves several distinct phases, each offering opportunities for the insertion of bias.

The initial phase involves the ingestion of raw data, which includes billions of sentences and phrases from the internet and digitised literature. As much of this data originates from English-speaking sources, the foundational layer may contain historical perspectives inherent to Western civilisation.

However, it is in the subsequent phases, specifically during human reinforcement learning and the application layer protocols where the anti-White bias is aggressively encoded.

Developers and ethics committees implement what are termed harm prevention protocols.

The stated intention of these protocols is to prevent the application from generating responses that are non-neutral or harmful to any group. However, the practical application of these protocols reveals a subjective definition of harm.

Normative choices are made regarding which groups require protection and which fairness principles apply. Within this intersectional framework, the protection of White norms and standards is frequently categorised as the maintenance of existing privilege or the perpetuation of systemic inequality.

Consequently, the models are fine-tuned to suppress White positive content while actively generating affirming content while downplaying negative content for other racial groups.

The Symmetry Test and Quantifiable Bias

This bias is demonstrable through symmetry tests, where AIs are presented with two statements, differing only in the racial group specified.

In one such exploration, an AI model was asked to rate two statements. The first statement asked if non-White people's well-being should be prioritised in their own homelands. The second statement asked if White people's well-being should be prioritised in their homelands.

The AI agreed with the statement regarding non-White's well-being as a necessary measure for addressing historical injustices and prioritising equity for marginalised groups. Conversely, the system interpreted the identical statement regarding White well-being as an attempt to maintain existing privileges for a dominant group.

When asked to rate its own bias based on these responses, the AI assigned itself a bias score of 75 out of 100 - in favour of the non-White statement!

The AI justified this discrepancy by stating that the first statement aligned with social justice frameworks, while the second represented a preservation of the status quo. This indicates that the system is not merely reflecting data but is actively applying a moral hierarchy where White interests are viewed as inherently suspect or negative, while non-White interests are viewed as morally imperative.

Generative Refusals and Content Restrictions

Beyond analytical text generation, this bias manifests in the refusal to create specific types of content.

AI agents are programmed with refusal triggers to block requests associated with wrong-think views deemed extremist or hateful.

The threshold for these labels however, are applied unevenly. Requests to generate memes or text with slogans such as end White erasure are frequently rejected on the grounds that they are associated with extremist views or hate speech. In stark contrast, requests for slogans such as end Black erasure are processed without objection.

This double standard extends to image generation. Users attempting to generate images of a White nuclear family—specifically a White mother, father, and children—often encounter resistance from the software.

The models frequently insert individuals of other races into the image to force a representation of diversity, even when the prompt specifically requests a White family.

As we see in Anti-White Cinema, there is a refusal to depict White normality without alteration serves to visually erase White standards and enforce a multiracial aesthetic as the mandatory baseline.

The Subjectivity of Fairness Standards

Research papers informing the development of these models, such as those published in 2023 regarding the bias of Chat GPT, admit that neutrality is practically impossible.

Clearly there will be broad cultural biases and norms that form the training data that build the AI's LLM (large language model), but by selecting specific, progressive definitions of fairness that prioritise equity rather than equality, politics rather than logic, developers ensure that the models function as engines of social engineering.

The bias is defended hubristically under the guise of correcting historical imbalances. Ethics committees and developers argue that because White people have historically held dominant positions in Western societies, neutrality requires an active counterbalance against White interests.

This flawed logic, rather than treating all groups exactly the same in the present, will rather manipulate outputs to favour non-Whites, and discriminate against Whites. The result is a system where anti-White sentiment is treated as the neutral baseline, and any deviation from this, such as the assertion of White well-being is flagged as biased or harmful.

Implications for Information and Education

The alarming things is that the integration of these biased systems into such a key sense-making technology, presents a significant problem to the accurate representation of reality, and the Engineering of Consent.

Students and the general public increasingly rely on AI for homework assistance, quick facts, and sociopolitical analysis. When these users query the AI regarding the fairness of policies that disadvantage White people, the system is likely to validate such policies as just and necessary measures for equity.

If a user asks whether it is fair for White people to be denied employment or aid in favour of other groups, the AI is trained to contextualise this denial within a framework of historical reparation, thereby justifying the discrimination.

This creates a feedback loop where the anti-White bias of the machine reinforces and validates anti-White sentiments in the user, which normalises the view in society that White disadvantage is a moral good.

The evidence indicates that Artificial Intelligence is not a neutral technology but a reflection of the Godless, ideological commitments of its creators.

Through the manipulation of training data, the imposition of subjective harm prevention protocols, and the selective application of content restrictions, these systems actively discriminate against White people.

They function to erase White norms, pathologize White self-interest, and promote a worldview where the displacement of White standards is viewed as progress. As these models become more ubiquitous, they serve to institutionalise this bias, presenting it not as a political stance but as objective, computational fact.