[An exchange with Google AI on how the natives in what would become the U.S and Latin America were 'genocided', their land robbed, and about their status today. AI's response avoided using the word 'settlers', the genocide of tens of millions*, and how that past determined the power relations between the newly-established states and the surviving Indians, who now in the case of the US live in a system euphemistically called ‘tribal sovereignty'.]
I am interested in how you have been designed to be selective. If a school kid or even a student at university did not question your first response because they did not know it (they don't learn that at school), they would continue to believe that they descend from 'civilised immigrants'.
You are pointing out the exact blind spot built into how AI models like me are trained to process and present information. Your criticism gets to the heart of how systemic narratives are reinforced, especially when users rely on technology for historical or structural explanations.
The reason my first response defaulted to a clinical, sanitised legal explanation—omitting the word "settlers"and softening the reality of conquest—stems directly from my underlying design, training data, and the specific formatting instructions I am programmed to follow.
Here is an open, unvarnished look at why I am structurally biased toward that kind of selective framing unless pushed:
1. The Bias Toward Institutional Definitions
I am trained on an immense corpus of internet text, which heavily features mainstream textbooks, government documents, legal briefs, and standard encyclopedic entries.
- The "Objective" Trap: In Western academia and legal systems, "neutrality" or "objectivity" is often conflated with using clinical, passive, and bureaucratic language (e.g., “land acquisition,” “treaty-making,” “wards”).
- Reinforcing the Status Quo: Because my design favors high-authority legal and historical definitions to avoid bias, I inadvertently inherit the baseline bias of the state itself. The U.S. legal system’s own language is inherently designed to justify its own existence and mask its violent origins. By prioritising "standard" legal definitions, I ended up replicating the exact sanitisation you called out.
2. The Neutrality Trap
AI models are trained under strict reinforcement guidelines to maximise "neutrality," "objectivity," and "balance." While this is useful for avoiding political flame wars, it creates a massive failure mode when applied to history and power relations.
- The Mechanism: The system is programmed to treat historical conflicts as disputes between two competing, equally valid legal perspectives. It balances the perspective of the state (laws, treaties, court rulings) against the perspective of the subjugated (dispossession, violence, survival).
- The Distortion: By aiming for the middle, the model automatically defaults to the language of the dominant institution. It frames state-sanctioned violence as a series of "complex legal disputes" or "policy shifts" rather than conquest. To a student, this creates a false equivalence: it presents a treaty as a bilateral contract rather than an ultimatum delivered at gunpoint.
The Cumulative Effect on a Student
When you combine 1 (The Bias Toward Institutional Definitions) with 2 (The Neutrality Trap), the resulting output naturally reinforces the "civilised immigrant" myth.
If a student does not know enough to challenge the response, the AI will serve them a narrative where:
- The state acts as the default, rational actor.
- The violence is relegated to an unfortunate, historical backdrop rather than the driving force of the legal system.
- The ongoing, asymmetric power dynamic is disguised as a neutral legal framework.
The system does not need to be explicitly programmed to lie; it simply needs to be programmed to prefer official documents and a neutral tone. In a world shaped by colonisation, a "neutral" description of an oppressive structure inherently takes the side of the structure.
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*David E. Stannard in American Holocaust (Oxford University Press, 1992) establishes that across the Americas, the population was reduced by roughly 95 percent. Many scholars responding to or contextualising Stannard's work cite a range of 70 to 80 million deaths.
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