Failure Types: Fabricated Facts, Wrong Attribution and Bad Arithmetic

Fabricated Facts

Fabricated facts represent one of the most straightforward yet deceptive forms of hallucination. These occur when systems generate information that sounds plausible but simply does not exist. In clinical settings, this might manifest as a medical AI system providing fictional patient symptoms or diagnostic criteria that have never been documented. A healthcare professional might receive a report stating that a particular medication has been proven effective for treating a rare condition, when no such clinical evidence exists. The fabricated nature of these claims makes them particularly dangerous because they often appear credible to untrained eyes.

Financial analysts working with AI-assisted research tools may encounter fabricated company data or financial metrics that have never been reported. An investment recommendation system might cite quarterly earnings figures for a company that has not released those results yet. The fabricated data appears genuine because it follows established formats and terminology. Technical support staff might receive system error messages containing made-up component names or software versions that have never been released. These false details often include proper technical terminology that makes them seem authentic to specialists who might not immediately question their validity.

• Medical AI systems providing fictional clinical guidelines or diagnostic criteria
• Financial tools citing non-existent company financial data or metrics
• Technical documentation containing made-up software versions or component names
• Legal research systems generating false case citations or statutory references

Wrong Attribution

Wrong attribution happens when systems assign information to incorrect sources or creators. This type of hallucination often involves genuine information being misattributed to the wrong person, organization or publication. In academic research environments, this might occur when AI tools claim that a particular theory was developed by a well-known academic when it was actually formulated by someone else. The system might provide accurate information but attach it to the wrong name or institution. Marketing departments using AI-generated content might attribute quotes or insights to industry experts who never actually said those things.

Content management systems may incorrectly credit articles or research papers to authors who wrote entirely different works. Technical documentation might misattribute software features or design decisions to the wrong development team or individual. Customer service chatbots might reference company policies or procedures that were actually established by different departments or at different times. These errors often compound when multiple systems reference the same incorrect attribution, creating a false trail of information that becomes increasingly difficult to trace back to its original error.

• AI research tools misattributing theories or discoveries to incorrect researchers
• Content management systems incorrectly crediting documents or articles to wrong authors
• Technical documentation misattributing software features to wrong development teams
• Chatbots referencing company policies from incorrect departments or time periods

Bad Arithmetic

Bad arithmetic involves mathematical errors that occur when systems perform calculations or make estimates that are clearly incorrect. These errors often happen when AI systems process data through complex algorithms that produce results which, while mathematically derived, are factually wrong. In budget planning, an AI tool might calculate projected costs that are wildly inaccurate due to incorrect data input or flawed calculation methods. The system might apply proper mathematical processes but arrive at wrong conclusions through faulty assumptions or data corruption.

Supply chain management systems might generate inventory forecasts that show impossible stock levels or unrealistic demand patterns. Financial reporting tools could produce balance sheet figures that don’t add up correctly or show impossible profit margins. Project management software might calculate timeline estimates that contradict basic scheduling principles or show durations that cannot possibly be correct given available resources. These arithmetic errors often appear reasonable at first glance but fail basic validation checks or contradict known facts about the situation being analyzed.

• Budget planning tools producing cost estimates that contradict basic financial principles
• Supply chain systems generating impossible inventory forecasts or demand patterns
• Financial reporting software creating balance sheet figures that don’t add up correctly
• Project management tools calculating timeline estimates that contradict scheduling logic

Each of these failure types requires specific verification approaches. Fabricated facts demand cross-referencing against established databases and authoritative sources. Wrong attribution requires tracing information back through documented sources and verifying original claims. Bad arithmetic involves checking mathematical consistency and validating results against known constraints. Practitioners should develop systematic approaches to identify these errors through pattern recognition, cross-verification and basic logical consistency checks. The key is to maintain awareness of these common failure modes and develop processes that catch them before they cause significant problems in practical applications.