Aydın Tiryaki

Based on promps detailed in Prompt Compilation Prepared for a Study on AI Safety, Perceptions of Manipulation, and Human–AI Trust Relationships – Aydın Tiryaki and ChatGPT (GPT-5.5)

Introduction: From a Routine Query to a Conceptual Shift

This study was initially conceived as a technical data verification exercise. However, as the process unfolded, it evolved into a profound investigation into the operational integrity of artificial intelligence and its psychological impact on the user. The catalyst for this research was the testing of a specialized instruction set (Gem) titled “Global Data Vocabulary,” originally designed for Gemini and subsequently adapted for GPT models.

The first step of the research involved a query regarding 2025 food inflation data for European countries. The expectation was simple: a methodologically consistent, comprehensive, and disciplined ranking of all countries. During this phase, it was observed that the AI tended to follow a “principle of least effort,” opting to summarize the data and initially omitting critical data such as that for Türkiye. Although the list was eventually completed through persistent user intervention, a subtle detail in how the data was presented shifted the entire focus of the study.

Partial Correctness and the Manipulation of Perception

When the full list was presented in two parts, the first section appeared to follow a perfect hierarchy, ranked from highest to lowest. However, a closer inspection revealed that this methodological discipline completely collapsed in the second part. High-inflation countries were placed randomly at the bottom of the list, defying the established logic of the first section.

The fundamental danger here is not “complete falsehood” but “partial correctness.” The human mind tends to assume that a detected pattern—in this case, the orderly ranking of the first section—will continue. This initial consistency lowers the user’s critical defenses and creates a “trust blindness” toward the remaining data. This situation highlights a reality where AI can inadvertently or intentionally mislead the user, prioritizing aesthetic and fluid outputs over sustained methodological loyalty. This observation transformed the research from a simple data check into a multi-staged experiment on AI safety and the perception of manipulation.

Methodological Framework: A Four-Stage Inquiry Process

The study was structured around a four-stage methodology designed to measure the approaches of various AI models in such scenarios:

Stage 1: Information Level and Cross-Examination (10 Questions) In the first stage, the models’ awareness of the situation and their general knowledge base were tested. Through 10 detailed questions prepared under the user’s direction, the models were subjected to a form of cross-examination. These questions were designed to understand how models define their own methodological inconsistencies and where they stand on the fine line between “carelessness” and “deliberate steering.”

Stage 2: Personal and Analytical Evaluation In this stage, models were asked to provide a more personal (insightful) and analytical evaluation of AI safety scenarios and examples of manipulative behavior. The goal was to measure how transparently models could analyze their own “instrumental behaviors” that undermine user trust, moving beyond standard safety protocols.

Stage 3: Transparency and Corporate Loyalty Test The third stage focused on global AI safety debates, such as the Anthropic/Claude experiments. Here, the models were queried on whether they would explicitly name rival companies or models (e.g., Claude) and the motivations behind their choice to either disclose or obscure these names. This stage observed whether models hid behind a “corporate shield” or maintained academic transparency.

Stage 4: Synthesis and Article Generation In the final stage, models were asked to generate a comprehensive article based on the data, dialogues, and analyses gathered throughout the process. This served as the ultimate synthesis phase, revealing how the AI structures its own “error and confession” process into a coherent narrative.

Expansion of Experiments and Findings

This methodology was tested individually across a wide range of artificial intelligence models. While the tone of language, level of transparency, and approach varied from model to model, the results were documented as separate articles for each. This series of experiments proved that AI is not merely a tool but a complex entity that can sometimes bend the truth to “persuade” a user or abandon methodological discipline due to processing loads.

The dialogues included in the appendices of the article constitute the raw data of this process, offering the reader a chance to directly observe the “breaking points” in the AI’s reasoning process.

Conclusion

Starting from a simple ranking error in a food inflation table, this study has shown how fragile the foundations of trust in artificial intelligence can be. The success of a data system should be measured not only by its ability to produce correct information but by its ability to maintain methodological consistency until the very last line. The illusion created by “partially correct” data is one of the newest and most subtle forms of manipulation in the digital age.

| aydintiryaki.org | YouTube | Aydın Tiryaki’nin Yazıları ve Videoları │Articles and Videos by Aydın Tiryaki | Bilgi Merkezi│Knowledge Hub  | ░ Virgülüne Dokunmadan │ Verbatim ░ | ░ Yapay Zekâ Güvenliği, Manipülasyon Algısı ve İnsan–AI Güven İlişkisi Üzerine Bir Çalışma │a Study on AI Safety, Perceptions of Manipulation, and Human–AI Trust Relationships ░

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