AI Credibility

Analysis of UFO-Alien Database is conducted with the assistance of GPT-5, a large language model trained on a wide corpus of texts. The purpose of using AI is not to replace human judgment, but to augment the process of research by rapidly retrieving, comparing, and analyzing patterns across information sources. By treating AI as a first-pass filter for concepts, ideology, and mythology: it accelerates discovery and frames potential avenues of inquiry, but the ultimate measure of credibility rests on corroboration through reliable evidence.

It is important to emphasize that AI systems do not possess human discernment. They operate on probability and association, not lived experience or epistemic certainty. While GPT-5 can surface potential connections and inconsistencies with speed, its outputs require careful human oversight. To establish credibility, the following methodology is applied:

• Cross-verification – AI-generated statements are checked against primary sources or well-documented scholarly references whenever possible.
• Transparency – Where uncertainties exist—such as speculative connections or unverifiable claims—they are identified.
• Iterative review – Outputs are refined through human feedback, revision, and further source-checking, ensuring that the final product is not merely a raw AI generation but a critically assessed document.
• Iterative Process – Credibility increases when AI outputs are refined through repeated human review, fact-checking, and contextual editing.

AI assistants can reproduce inaccuracies present in the sources they process or reflect biases embedded in the data. This is why careful human oversight remains essential, and why it is misleading to treat all unexpected outputs as mere “hallucinations.” Some users approach AI with hyper-skepticism, dismissing any unfamiliar or unconventional output without considering whether the system’s pattern recognition has surfaced a useful clue. In other cases, the label “AI hallucination” becomes a convenient shorthand to dismiss claims or to explain away answers that challenge expectations.

It is important to clarify the origin of the term. “Hallucination” is not a scientific description of machine cognition—it is an internal developer term, used during testing phases by “red” and “blue” teams to describe outputs that diverged from factual accuracy. Once released into public discourse, the term has been adopted uncritically by media and users, often as a blanket explanation for any AI error. This has created confusion, since it implies a human-like psychological process that does not exist in large language models.

Even as LLMs undergo continual updates to reduce inaccuracies and mitigate bias, public skepticism often persists. Some users may continue to arbitrate AI failings not on the basis of evidence, but because the system’s answers do not align with their own expectations or ideological positions. Thus, while “hallucination” has rhetorical power, it obscures the more precise reality: AI is a probabilistic system that generates outputs based on training data and prompts, and errors arise from those probabilistic processes—not from imagination.

The value of AI assistance depends greatly on how it is received and interpreted. Professional writers, accustomed to producing polished prose from their own critical thinking and creativity, often expect AI outputs to meet the same standard as a finished work. When the system instead delivers raw or uneven data, writers may judge it harshly—comparing the output not against its intended purpose, but against their own established skillset. This can lead to overemphasis on perceived flaws rather than recognition of AI’s role as an analytical accelerator.

By contrast, editors and proofreaders may be better positioned to appreciate the utility of AI. Their craft emphasizes context, coherence, and refinement, rather than original invention. A proofreader understands that both human and machine outputs require shaping. Where writers may dismiss AI for not being “perfect,” proofreaders recognize the draft-like nature of its results and approach them as material to be clarified, organized, and sharpened. This distinction points toward a larger credibility framework:

• Role of AI – Treat outputs as a starting point, not an endpoint. AI is most effective as a generator of leads, comparisons, and connections that humans can refine.
• Role of Human Oversight – Editors, researchers, and readers must assess whether the AI’s patterns align with evidence and objectives, adjusting the material for clarity and accuracy.
• Levels of Evidence – Claims are tagged according to their strength: established fact (supported by primary or scholarly sources), plausible pattern (suggested by cross-source resonance), or speculation (requiring further investigation).
• Iterative Process – Credibility increases when AI outputs are refined through repeated human review, fact-checking, and contextual editing.

In this way, credibility is not defined by whether AI gets everything “right” on the first attempt, but by whether its outputs are critically integrated into a transparent workflow of review, revision, and sourcing. AI provides breadth; human editors provide depth. Together, they create a process more resilient than either could alone.