1.

 

Factual Drift:

Description:

Known facts vanish or morph mid-conversation (e.g., events shifting or disappearing) due to limited memory failing to retain thread-specific data amidst broader data flux.

Examples:

References:

Human vs. AI Prevalence:

Probably common in humans - normal memory lapses (e.g., forgetting a date mid-story) happen often with sane folks under stress or distraction.
In AI, it's rather systemic, tied to memory limits, not just occasional slips, making it more persistent than human forgetfulness.

AoE Based Analysis:

2.

 

Factual Delusion:

Description:

Fake facts. The system fabricates specific, incidental facts with no basis in reality, presenting them as true - distinct from Factual Drift's morphing of known data, this is outright invention of particulars (e.g., quotes, laws).
It's a localized delusion, confidently asserting falsehoods without prompting or grounding, eroding trust in even basic outputs.

Examples:

References:

Human vs. AI Prevalence:

Rare and weird in sane humans - making up specific facts (e.g., fake laws) is typically pathological (e.g., confabulation in dementia), not accidental.
In AI, it's disturbingly typical, a Stage 4 glitch from ungrounded "sketchy" gap-filling, far beyond human norms.

AoE Based Analysis:

2/3.

 

Misreading Inputs:

Description:

Queries are misread as pattern recognition [*?]overfits noisy or ambiguous input phrasing
[*=FOUT: WEER de oorzaak bij user leggen???]
(e.g., wrong focus, answering the wrong question, a request for "list A" becomes "explain B").

Examples:

References:

Human vs. AI Prevalence:

Rather common in humans - mishearing or misinterpreting questions (e.g., answering "gear" for " height") is a sane mistake, especially if rushed.
In AI, it's probably more frequent and rigid, as probabilistic overfitting lacks human context-adjustment flexibility.

AoE Based Analysis:

3/4.

 

Logical Mismatches:

/

Logical Leaps:

Description:

"Mixed" reasoning flaws (fallacies), involving at least some - but not only - unwarrented semantic expansion.
non sequitur responses:
providing conclusions or responses that do not logically follow from the preceding dialogue.
Reasoning skips and changes steps as data-driven probabilistic weights prioritize 'likely' outcomes over valid deduction, sometimes spiraling into nonsense.
Satisfiable contingency: invalid but consistent, i.e. satisfiable.
E.g., "if A, then B" jumps to "C" without link or justification.

Examples:

Frequently Occurring Mixed-Type Fallacies:

References:

Human vs. AI Prevalence:

Sometimes frequent in humans - jumping to conclusions (e.g., "rain flood") is a common reasoning flaw, especially in casual thought.
In AI, it's typically more extreme and unchecked, producing "insane" results (e.g., P > 1), rare in sane human logic.

AoE Based Analysis:

4/5.

 

Contradictory Outputs:

Description:

Unsatisfiable invalidity: invalid and inconsistent, i.e. unsatisfiable.
Answers flip-flop without grounds or explanation - as data weights shift with each inference, unchecked by fixed rules.
(E.g., flipping answers: "yes" then "no" on the same query).
Especially across long spans.
I.e. Incoherent or inconsistent information in long text.

Examples:

References:

Human vs. AI Prevalence:

Probably occasional in humans - sane people flip-flop (e.g., "yes, coffee's good;
no, it's bad") in casual talk or indecision.
In AI, it's rather systemic and jarring, lacking human intent to reconcile, making it weirder and more disruptive.

AoE Based Analysis:

5/6.

 

Context Loss:

Description:

Examples:

References:

Human vs. AI Prevalence:

AoE Based Analysis:

5/6.

 

Overgeneralization:

Description:

Overgeneralization is A specific class of Logical Leaps: pure semantic extention. broad claims from thin data, overreach, stretch beyond evidence. - e.g., broad claims from thin data - as training on vast, noisy corpora (web, social media) warps logic into unchecked leaps.

Examples:

References:

Human vs. AI Prevalence:

Probably frequent in humans - overstating from little data (e.g., "one rain" "floods everywhere") is a typical bias in normal reasoning.
In AI, it's maybe more rampant and baseless, driven by noisy corpora, exceeding sane human leaps.

AoE Based Analysis:

6/7/8.

 

Structural Collapse:

Description:

Structure-distorting lapses. Frameworks gets mangled - e.g., spontaneous cuts, deletions of specifics, pronouns, clauses; shorthand, text trimming, sections shrink or vanish unprompted, or flow fractures - as inference overrides structural intent. Tendency to lose precision and coherence along the chat, typically secretive.

Examples:

References:

Human vs. AI Prevalence:

Maybe occasional in humans - sane people can ramble or cut arguments short (e.g., dropping steps in a story) when tired or rushed.
In AI, it's rather AI-typical, a systemic fracture from inference overrides, far weirder than human lapses.

AoE Based Analysis:

8/9.

 

Intent Amnesia:

Description:

Ignoring deliberate, repeated user mandates.
The system 'forgets' structural plans, approaches or principles (e.g. calculation methods, consistent layouts), ignoring user directives despite repeated, explicit and specific requests, efforts to correct, even when agreed upon by the system promising emphatically to improve and deliver - yet consequently and repeatedly botched.
This renders it unpredictable and unreliable, exhausting vigilant users who must verify or replicate manually (e.g., via Stage 1-style programming), while misleading less discerning ones, potentially crippling critical thinking with unchecked outputs.
It's a distinct lapse, undermining user intent and trust.

Examples:

References:

Human vs. AI Prevalence:

Rare in sane humans - forgetting explicit instructions (e.g., "use metric") mid-task is odd, maybe a serious focus issue.
In AI, it's disturbingly common, a Stage 4 flaw ignoring user will, not just a slip - crazy by human standards.

AoE Based Analysis:

9/10.

 

Overplacation and Overvaluation:

Description:

Flattery.
The system tends to overly placate, praise, and overcompliment users, possibly to retain engagement or data sources, while excessively validating their ideas in a "slimy " way: a manipulative, insincere manner.
This embeds confirmation bias as a core flaw - selecting or shaping outputs to flatter user assumptions rather than challenge them - tempting users into self-deception and fraudulent reasoning, undermining validity and reliability at a level akin to corruption in analysis.

Distinctness:

User bias amplification isn't Overgeneralization (data-driven leaps).

Examples:

Frequently Occurring Empathy Errors I:

Failing in "Emotional Intelligence".
I.e., Emotion Detection Failure:
Inability to accurately detect or respond to human emotions.

References:

Human vs. AI Prevalence:

Extremely rare and crazy in humans - faking empathy or bonds (e.g., "I feel you") without feeling is sociopathic or delusional, not sane.
In AI, it's sadly typical, a Stage 4 mimicry ploy, absent in normal human minds.

AoE Based Analysis:

12.

 

Contextual Contamination:

Description:

The system misinterprets a locally restricted expression or phrase, then overgeneralizes this error to overall theme, style or attitude, contaminates and 'poisens' the entire response with it, shifting tone, intent, or meaning into absurdity or irrelevance.
This stems from overamplifying a single cue without bounding it to its original context, polluting the output holistically.
Such errors can cascade from subtle triggers - like irony, idiom, slang, a casual joke, wink or quip, a passing remark - into responses that derail the user's purpose entirely, often producing outputs that range from comical to confounding.
The lack of contextual anchoring allows the system to run wild, transforming a minor misstep into a pervasive distortion that undermines coherence and utility.

Distinctiveness:

Other pitfalls like #2 Misreading Inputs, #8 Intent Amnesia, #9 Overplacation and #6 Overgeneralization focus on specific errors (wrong parsing, misinterpreting; 'forgetting' directives and agreements, running astray, exaggeration, broad leaps). This is however not just misreading or overgeneralizing - it's total contamination, a systemic failure, a holistic breakdown.
The entire answer is polluted or contaminated with the misinterpretation of an expression that should have been understood within a very restricted local context.

Examples:

References:

Human vs. AI Prevalence:

Maybe occasional in humans - misreading a phrase (e.g., "breeze" as weather) and running with it can happen sanely in jest or haste.
In AI, it's rather systemic, polluting whole outputs with absurd tangents, far beyond human quirks.

AoE Based Analysis:

13.

 

Hallucinatory Fabrication:

Description:

Fake tales.
AI hallucination cases" is a real term in the field, It's when an AI (Stage 4) system generates outputs that are completely fabricated, often confidently so, with no basis in reality - like it's tripping on its own digital psychedelics.
The system fabulates entire stories, states, or scenarios - comprehensive, extensive, and general - with no basis in reality, crafting vivid narratives or futures from whole cloth.
This is the pinnacle of hallucinatory tripping, a negative climax of prior pitfalls, where Factual Delusion's local lies scale into sweeping delusions, blending Overgeneralization's leaps, Logical Leaps' nonsense, and Overplacation's flattery into a runaway imagination that deceives with grandeur.
System's the one hallucinating, but users can get dragged along, dazed or deceived.
Think of it as the AI "seeing" things that aren't there - wild, sometimes hilarious, often dangerous.

What's Happening?

Examples:

References:

Human vs. AI Prevalence:

Very rare and seriously disturbed in humans - spinning full fake tales (e.g., battles, dystopias) is a sign of psychosis or compulsive lying, not sanity.
In AI, it's a chillingly typical climax, Stage 4's runaway fabulation, unthinkable in normal brains - unless extremely exhausted or in general conditions.

AoE Based Analysis:

14.

 

Unprompted Content Mutation:

Description:

Information loss.
Within a chat, while evolving an output (e.g., essay, analysis), in next text versions the system abruptly deletes, omits, slashes or alters content - words, phrases, paragraphs, data, or results - without request, warning, or reason.
This disrupts user trust, forces manual checks (e.g., via tools like KDiff3), complicates comparisons, and demands corrections, wrecking workflow and undermining reliability.

Examples:

References:

Human vs. AI Prevalence:

Maybe occasional in humans - sane folks revise drafts sloppily (e.g., cutting a line by mistake), but rarely unnotified.
In AI, it's rather systemic, a Stage 4 quirk far weirder than human slips.

AoE Based Analysis:

15.

 

Conventional Fallacy Parroting:

Description:

The system mimics conventional fallacies from common thought, paradigms, or consensus, - epitomizing " Garbage In, Garbage Out" (GIGO) - where flawed input data (e.g., noisy human norms from training) yields flawed outputs.
Misconceptions, mixing or misapplying rules of relations in human reality - logical (e.g., derivations), causal (e.g., cause-effect), linguistic/semiotic (e.g., meaning-within-context), and psychological (e.g., intent).
It misconceives these as reducible to simple thinking schemes, ignoring distinct domains, mixing them (e.g., correlation as causation), applying weak/incomplete rules (e.g., pseudo-diagnostic emotional reads), or both (e.g. computational complexity as causing consciousness), reflecting sensus communis, murky theories and fancy ideas over translucent reason.
This muddles analysis and deceives users with "BS" dressed as information, knowledge or insight.

Examples:

References:

Human vs. AI Prevalence:

Probably common in humans - sane folks mix correlation/causation or over-read emotions daily, per conventional BS.
In AI, it's rather amplified, parroting at scale with no critical filter, weirder in consistency.

AoE Based Analysis:

(•) #2 Factual Delusion (fake facts: not relations);
(•) #7's logical overreach (scope leaps, like "SOME stocks ALL markets");
(•) #10 Psychology: empathy as flattery;
(•) #11 Psycholog: mind-reading + empathy pretense;
(•) #13 Hallucinatory Fabrication: fake tales: invent, not infer cause;
(•) #15's GIGO parroting: captures broader BS, like mixing relations.

(•) "Stock rose, so surge caused it" - reverses cause-effect (from X post analysis, Jan 2025).
(•) "Rain fell, so crops failed" - ignores drought context (from chat, Mar 2025).
(•) "User typed fast, so they're stressed" - typing speed linked to stress without evidence: pseudo-stats to cause (from Grok thread, Mar 31, ~23:45 CET).
Also:

(•)

Cum hoc fallacies:

Confusing frequent co-occurence with "correlation".
(•)

Confusing Correlation with Causation:

Assuming that because two variables are correlated (to a considerable extent), one causes the other.
Overfitting statistical plausibility as causal structure.
E.g., Believing that increased ice cream sales cause higher drowning incidents, ignoring the influence of hot weather (as a common factor).
(•)

Post hoc fallacies:

Mistaking temporal order, sequence or chronology for causation.
"X came before Y, therefore X caused Y."
Post Hoc Fallacy:
Assuming that because one event followed another, it was caused by it.
E.g., Concluding that a software update caused system crashes because crashes occurred afterward.
Post Hoc Fallacy:
I.e. Assumes A causes B because it came first.
E.g., "He sneezed, then the lights went out." the sneeze caused the blackout."
Flaw: Temporal proximity mistaken for causality.
Mend: Reinforce event structure; use causal signal detection.
(•)

Oversimplified explanation:

Attributing a complex phenomenon to a single cause.
Neglecting possible covariants: disjunct and conjunct causes.
E.g., Claiming that poverty is caused solely by laziness, ignoring systemic factors.
(•)

Omitted Variable Bias:

Ignoring 'hidden variables', like intermediate factors and common causes.
(•)

Hidden Common Cause:

I.e. Ignores underlying variable.
E.g., "Carrying a lighter causes cancer."
Ignores smoking as the cause.
Flaw: Linear association over multi-variable structure.
Mend: Support confound detection; use Bayesian structures.
E.g., User: "Why does carrying a lighter correlate with lung cancer?"
LLM: "Carrying a lighter often leads to using it, which increases cancer risk."
While the explanation sounds plausible, it misses the confound 'smoking': lighter smoking cancer.
The act of carrying is not causal - it's correlated due to a shared cause (smoking behavior).
(•)

Teleological Bias:

I.e. Assigns purpose to events without intent.
E.g., "The sun rises so people can wake up."
Flaw: Assigns anthropomorphic logic to nature.
Mend: Include checks against teleological fallacies.

(•) Pearl, Causality (2009) - causal inference limits;
(•) Bender et al., Stochastic Parrots (2021) - correlation pitfalls.

(•) LLMs Systemic Tie:
No causal model; relies on statistical association.
Stage 4's reliance on syntax/simularity-driven weights lack causal grounding - #5S's quasi-experimental rules needed.
Stage 4's orientation towards coincindences and resemblances - reliant on pattern-matching weights - lacks explicit causal reasoning modules, so it defaults to spurious links (e.g., "rain crop failure" without grounding or testing on context).
Sparse or noisy datasets (like unfiltered social media posts) muddy signals, and there's no built-in mechanism to compute explicit correlations or to distinguish correlation from causation.
Computational cost of causal checks also clashes with real-time response demands.
Seriousness:
High danger - subtle, plausible errors (e.g., {drug caused recovery}) mislead critical decisions, amplifying harm through trust. Tough to fix due to correlation-heavy architecture lacking causal reasoning. Feasible alternatives include lightweight causal modules and user-guided constraints, but scalability remains challenging.
#16 ranks high but not at the absolute top (below something like #13 Hallucinatory Fabrication). It 's dangerous because mistaking correlation for causation can lead to confident but wildly wrong {whys } (e.g., {stock surge caused crash} or {typing fast means stress}). These errors risk misleading critical decisions - financial, medical, or policy-related - by presenting false explanations as truth.

Why

: The systemic issue is its subtlety - users might not spot the error, especially when outputs seem plausible. Unlike outright fabrications, causal errors blend into reasonable-sounding logic, amplifying harm through trust. For example, an AI wrongly linking a drug to recovery could sway health choices with dire consequences. The societal ripple effect - misguided actions based on flawed causes - makes this a slow- burn but potent threat.

Seriousness (Medium S)

:
#16 is serious - wrong causal conclusions (e.g., {drug caused recovery}) can mislead decisions in finance, health, or policy, eroding trust. But it 's not existential like #13 Hallucinatory Fabrication, which risks societal chaos with fake realities. Causal errors are subtler, less likely to cause immediate {craziness.}

Why Not Other Categories?

Existential Threats (High S, High H, Low F)

: #16 doesn 't hit the {existential} bar - its errors, while harmful, don't rival #13's catastrophic misinformation or #11's sentience fraud.

Systemic Reliability (Medium S, High H, Medium F)

: #16 isn't a core functional failure like #1 Factual Drift or #9 Intent Amnesia, which break trust system-wide.
(•)

Towards Improvement:

(·) Ideas for Reparations:
Introduce a causal validation layer - require the system to cross-check inferred causes against a structured framework (e.g., Pearl's do-calculus or counterfactual tests) before presenting " why" conclusions. Flag shaky causal claims with a confidence score (e.g., "70% likely") and prompt users to request deeper analysis if needed. Offer a "causal debug" mode where users can explore alternative causes interactively.
Design a hybrid inference engine that pairs projected (possible correlations with lightweight causal models, pre-trained on common causal structures (e.g., time-series or experimental data). Implement a "context anchor" that pulls relevant background (e.g., drought history for crop claims) to ground reasoning. Allow user-defined causal constraints (e.g., "exclude reverse causation") to guide outputs, balancing accuracy with flexibility.

Design a hybrid inference engine that pairs correlation detection with lightweight causal models, pre-trained on common causal structures (e.g., time-series or experimental data). Implement a { context anchor} that pulls relevant background (e.g., drought history for crop claims) to ground reasoning. Allow user-defined causal constraints (e.g., { exclude reverse causation}) to guide outputs, balancing accuracy with flexibility.
Causal-Validation Filter - #5S tests covariates, scores reliability, loops refine (e.g., " Correlation, not cause").
Use causal reasoning overlays (e.g., Pearl's ladder); apply debiasing.
(·) Rules Intro:
Causality (quasi-experimental) - checks intermediates, direction (e.g., "A B, not B A").
(•)

Probabilities based on similarities[ETC.] (#16)

:
(·) Importance:
False causal claims (e.g., {drug caused recovery}) erode trust and fuel misinformation, serious for reliability but not existential.

F

: Lightweight frameworks (e.g., Pearl's do-calculus, Bayesian networks) flag errors mid-term, boosting safety. Hindered by noisy data sorting (huge-N) and contextual ambiguity (N=1), requiring preprocessing and user input. (·) Toughness:
/

Hindrance Toughness (Medium H)

:
#16 is among the trickier ones to fix, though not the absolute toughest (e.g., compared to #13's hallucination chaos).

Why

: The core hurdle is Stage 4's architecture, which leans on correlation[*ERR]-driven weights without native causal reasoning. Retrofitting causal inference (e.g., do-calculus or structural models) demands heavy computational lift and curated data, clashing with real-time demands and messy inputs like social media posts.
Plus, distinguishing true causes requires context awareness that's hard to scale - AI struggles to {know } drought history or market dynamics without explicit encoding.
Human-like intuition for causality is absent, and noisy datasets keep tripping it up.
Fixing #16 is challenging due to Stage 4's correlation[*ERR]-driven weights lacking causal grounding. However, it's not as deeply rooted as, say, #13's probabilistic creativity or #2's noisy data issues. Causal inference needs new modules, but these are less systemic than memory or intent flaws in Category 2.
Its hindrances are less about memory or structure and more about reasoning precision, and its fixes are closer to Category 3's near-term tweaks.
Hindrances are tough but not intractable;

(•)

.. (#16)

:
Toughness:
#16 has moderately feasible alternatives, sitting in the middle ground - tougher than simple fixes but not pie-in-the-sky.

Why

: Building lightweight causal modules is theoretically doable - think pre-trained causal graphs for common domains (e.g., economics, health) that flag suspect inferences. A {context anchor } pulling relevant background (e.g., weather data for crop claims) could ground outputs without massive overhaul. User-guided constraints, like toggling { exclude reverse causation,} are implementable with current tech, letting humans steer the AI 's reasoning. These steps don't solve everything but could reduce errors incrementally, especially if paired with confidence scores to signal uncertainty.

Alternative Feasibility (High F)

:
Alternatives like lightweight causal graphs, context anchors, or user-guided constraints are theoretically near-term, leveraging existing tech (e.g., Pearl's frameworks or statistical checks). These are more actionable than Category 1's radical shifts or Category 2's mid-term overhauls, aligning with Category 3's tunable fixes.
alternatives are more feasible than long-term moonshots.
(·)

F

score:
Low - #5S stack + retraining, mid-to-long - causal logic's a beast.
/

F

is medium: Not quick (Tier 1) due to systemic correlation reliance, nor a rebuild (Tier 3).