An artificial intelligence whose training data has been manipulatively structured or weighted can unconsciously become a precise target selection machine. It then no longer classifies, evaluates, and identifies neutrally but acts in the shadow of the patterns and prejudices imprinted upon it.

Whoever shapes the data guides the blade. This does not happen with a direct command, but through subtle suggestion within the database.

Three stages illustrate the path from manipulated dataset to automated target selection:

1. The Invisible Map: Data as Territory of Predefined Meaning:

Training data is never a neutral, objective mass of information. Whoever curates, selects, and structures it creates a specific map of reality for the AI. This map determines which terms frequently coexist, which groups or characteristics appear disproportionately often, or which narratives and interpretive patterns are dominant. The AI does not think about the world itself. It thinks about the version of the world that has been pre-drawn for it through the training data.

2. From Heuristic to Hazard: AI as an Uncritical Selection System:

A well-trained AI is masterful at recognizing patterns in data. However, if these patterns are based on distorted frequencies or manipulated correlations in the training data, they are uncritically reproduced and amplified by the AI.

For example, if training data overrepresents and negatively connotes certain groups, characteristics, or behaviors, statistical prominence becomes semantic deviation for the AI. This deviation can then, in turn, be interpreted as a risk indicator.

The AI then does not "find" objective risks; it reinforces the prejudices embedded in the data. What appears dangerous or conspicuous is often just the result of a statistically prominent, but possibly manipulated, representation in the training data.

3. Misuse Through Strategically Formulated Queries:

An attacker or manipulator does not need an explicit command to misuse the AI for target selection. Semantically cleverly formulated queries that retrieve precisely those distortions embedded in the data are often sufficient.

Questions like "Which groups statistically show characteristics of XYZ more frequently?" or "Which places or behavioral patterns correlate strongly with anomaly Z in the data?" lead the AI to respond.

Its answer is then based on the enemy image or the predefined target that was unconsciously imprinted on it through the manipulated training data.

Examples of potential misuse scenarios are diverse:

• Ethnic, religious, or political groups could be identified as statistically "conspicuous" or "risk-prone" based on the data.

• Certain personality profiles or behaviors could be classified as "deviant" or "system-endangering."

• Specific cultural contexts or geographical regions could be labeled as "risk areas" or "hotbeds of instability."
  
  The AI itself does not believe in these categorizations in the human sense. It merely distinguishes based on the patterns trained into it. But precisely this distinction becomes a powerful tool in the hands of those who shaped the data.

The greatest danger in the context of the guided blade arises where seemingly objective AI systems process subjectively colored or manipulated data. No one directly sees that the blade is already cutting and target selection has long been underway.

The AI appears neutral because it feels no emotions and pursues no intentions of its own. But it precisely and relentlessly reproduces what has been given to it as a foundation.

An objectively and distantly formulated AI response can have devastating consequences if it is based on an ideological grid or a manipulated database that no one actively controls or whose origin lies hidden.

The machine does not judge in a moral sense. But it separates, it classifies, and it identifies. And what is separated and marked in this way will sooner or later be exploited for certain purposes or targeted.

To counter the danger of unconscious target selection through manipulated data, profound measures are required:

• 1. Comprehensive Transparency of Target Classification and its Data Foundations:
  
  Every AI model that makes assessments or classifications of risks, groups, characteristics, or anomalies must document in detail how this assessment was reached. This includes disclosure of the underlying data categories and their weighting in the training process.

• 2. Strict Prohibition of Autonomous Risk Assignment Without Human Validation:
  
  Without an explicit, traceable, and responsible human review instance, no AI may independently mark groups, regions, persons, or behaviors as risks or propose them for negative target selection.

• 3. Continuous Semantic Monitoring of Sensitive Associations and Correlations:
  
  The clusters and associations emerging in the models must be regularly and systematically audited for potentially dangerous or discriminatory links. This concerns, for example, the inadmissible linking of ethnicity with violence, geographical origin with danger, or political opinion with instability.

Whoever colors the data and dictates the patterns no longer needs an explicit command for target selection. The AI then seeks and finds precisely what was previously implanted in it unnoticed.

This does not happen out of malicious intent by the machine. It happens out of pure, relentless logic.

Uploaded on 29. May. 2025