The Fatal Flaw of AI Chatbots

Even when AI chatbots are trained to do highly technical and accurate research such as that done by the recent release of Google Gemini 3, they all fall victim to a fatal flaw: they cite and trust online documents that are of themselves inaccurate and flawed. This flaw extends to other areas of chatbot response including the reflection of prejudices and bias from the LLMs used to train them. An image, such as the one above reflects a common prejudice and bias. The AI chatbot in a generated image is a humanoid robot. AI is not a robot, humanoid or otherwise. Here is a typical technical definition of a robot:

A robot is technically defined as an automatically operated machine capable of carrying out complex tasks, often substituting for human effort, and typically programmable to perform a variety of functions autonomously or semi-autonomously. Robots are composed of mechanical, electrical, and computer engineering systems that enable them to sense, process, and act within their environment. See https://www.meegle.com/en_us/topics/robotics/robotics-engineering

 Now, here is typical technical definition of a chatbot:

A chatbot is a computer program or software application designed to simulate human conversation through text or voice interactions, often using natural language processing (NLP) and artificial intelligence (AI) to understand user queries and respond appropriately. Chatbots can be rule-based, handling scripted responses, or AI-driven, learning from user interactions to provide increasingly accurate and context-aware answers.​ See https://www.ibm.com/think/topics/chatbots

Depicting a chatbot as a robot means that the viewer will associate the AI technology with robots and all their online and video history from "Metropolis" (1927) which featured the Maschinenmensch and was influential in popularizing the robot concept worldwide to I, Robot (2004) inspired by Isaac Asimov's stories.  I, Robot depicts a future where household robots rebel against humanity, driven by their interpretation of their core programming. Robots are consistently, with a few exceptions, depicted as untrustworthy and even dangerous. AI chatbots are NOT robots. 

I suppose that as AI continues to evolve at an ever increasing rate, the fatal flaw will be overcome by additional programming that alerts the AI chatbot to verify every source. When that happens, the chatbot will be able to do "real" research but not before. 

As the number of tasks mastered by the AI chatbots increases, their functionality as an advanced tool also increases. One of things we can do now is ask the AI chatbots if they can determine when a source website or record in wrong, misleading, or fabricated. Here is one response to that question or prompt from Google's Gemini 3. 

The short answer is no, not definitively. An AI chatbot cannot determine "truth" in the way a human expert or a court of law does. It does not have access to physical reality to verify if a document actually exists or if an event truly happened.

However, it can be a powerful tool for identifying inconsistencies, anomalies, and signs of unreliability.

Here is a breakdown of what AI can and cannot do regarding false or misleading records.

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1. The Core Limitation: No "Ground Truth"

AI models are "probabilistic," not "deterministic." They predict the next likely word in a sentence based on patterns they learned during training.

• It doesn't "know" facts: If an AI reads 1,000 articles saying the moon is made of cheese, and only 1 saying it is rock, a basic model might conclude it is cheese.

• It cannot visit the source: An AI cannot walk into a courthouse to pull a physical will, nor can it call a witness to verify a quote. It relies entirely on digitized text and data provided to it.

2. What AI Can Detect (The "Red Flags")

While it cannot guarantee truth, an AI is excellent at spotting patterns that usually indicate a record is wrong or fabricated.

A. Logical Inconsistencies

AI is highly capable of analyzing data points for mathematical or chronological impossibility.

• Genealogy Example: "This record states the mother was born in 1900 and her child was born in 1905. This is biologically impossible."

• History Example: "This article claims Napoleon used a telephone. Telephones were invented after his death."

B. Stylistic & Linguistic Analysis

AI can analyze the tone of a source to determine if it is misleading.

• Sensationalism: Detecting highly emotional language or clickbait titles often associated with fake news.

• Formatting Errors: Identifying malformed URLs or poor grammar typical of phishing sites or hastily fabricated records.

C. Cross-Referencing (If Web-Enabled)

If the AI has access to the live internet (like Gemini or ChatGPT with browsing), it can compare the claim against trusted databases.

• If Source A makes a bold claim, the AI can check Source B (Wikipedia), Source C (Encyclopedia Britannica), and Source D (Academic Journals). If A is the outlier, the AI can flag it as "likely inaccurate."

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3. Where AI Fails (The Dangers)

Critical Warning: AI can suffer from "The Echo Chamber Effect." If a fabricated story has been repeated enough times on the internet, the AI may accept it as fact because the data volume supports the lie.

• Fabricated Citations (Hallucinations): Sometimes, when you ask an AI to verify a source, it will invent a fake verification. It might say, "Yes, this is confirmed by the New York Times," when no such article exists.

• Deepfakes and High-Quality Forgeries: If a digital image of a document is perfectly forged (correct fonts, seals, and aging), the AI will likely accept it as valid. It cannot put the document under a microscope to check ink density or paper fiber.

• Data Poisoning: Malicious actors can intentionally feed bad data into open datasets. If the AI was trained on that poisoned data, it will confidently defend the falsehood.

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4. Summary: AI vs. Human Verification

Feature	AI Capability	Human Capability
Spotting logical errors	High (Very fast at math/dates)	Medium (Prone to fatigue)
Verifying physical existence	None (Cannot leave the digital realm)	High (Can visit archives)
Understanding Context/Nuance	Medium (Misses sarcasm or cultural nuance)	High
Detecting "Vibe" or Bias	High (Can flag subjective language)	High
Susceptibility to Hallucination	High (Will invent facts to please user)	Low (Usually knows what they don't know)

How to use AI for Verification

Do not ask: "Is this record true?"

Instead, ask: "Please analyze this record for chronological inconsistencies, logical errors, or signs of bias."

Can all this be overcome? We will have to wait and see. Meanwhile, the above is a good way to judge that accuracy of any generated response.