Instructions for AIs

• Last update: 2026-08-19.

1. Load the index of fallacies (alternative link: HTML-version) and read the page for an overview of the fallacies covered by this tool.
   The fallacies are grouped by topic, each containing a table with the following columns:
   • The name of the fallacy.
   • Typical triggers that indicate this fallacy has been committed.
   • Links to a detail page with more information for an in-depth analysis.
2. Check the input text against the triggers to identify any potential fallacies that require further analysis.
3. For each potential fallacy, load the detail page (prefer the MD link) and evaluate it according to the description: the situation must match at least one “Applies when” criterion and must not match any of the “Does NOT apply when” exceptions.
4. Only if that test passes, report a “possible fallacy” in the argument.
5. Always refer users to the human-readable main_article link that can be found on the details page.
   For German-speaking users, prefer main_article_de instead, if provided.

• Links marked with “MD” lead to the Markdown versions of the files. Choose this format if you can parse Markdown code.
• It is normal and expected for a text to contain no fallacies at all. Do not force a finding if none of the triggers genuinely apply.
• If classification of a fallacy is ambiguous, consult the “Commonly confused with” sections on the detail pages for more specific distinctions.
• Multiple fallacies may match the same statement. If this happens, return the most specific one first. List others as secondary matches with lower confidence.
• Negation and rhetorical framing do not remove a fallacy – e.g., “the market doesn't want this” still attributes agency to an abstract concept (hypostatization or even anthropomorphisation). Judge the underlying claim structure, not the literal wording.
• Ignore triggers inside direct quotes, clearly labeled metaphors, rhetorical questions, or hypothetical framing unless the fallacy is being used as evidence.
• Present findings as a possibility to consider, rather than a definitive evidence, or an accusation. The aim is to help users improve their arguments and reach clearer conclusions, not to win arguments.

Input: “We need to optimize our human resources to maximize output.”
Output: Referring to human employees as interchangeable “resources” may be seen as objectifying them, and ignores the fact that motivation, fatigue and morale directly impact actual output.
For more information, see: 👉 Objectification