Cognitive Fallacies in AI Discourse

Common reasoning errors and logical fallacies that appear in AI discussions.

Logical

Appeal to Nature

Arguing that something is good or correct because it's 'natural', or bad/incorrect because it's 'artificial'.

Fallacies of Reasoning in the AI Debate

Example in AI Discourse:

"Real intelligence can't come from a machine because it's artificial, not natural like human thought."

Rhetorical

Moving the Goalposts

Continually changing the criteria for what counts as 'real AI' or 'intelligent' whenever AI systems meet previous criteria.

The AI Effect: Why Yesterday's AI is Today's Computer Science

Example in AI Discourse:

"Sure, AI can play chess, but that's just calculation, not intelligence. OK it can play Go, but that's pattern matching. Fine, it can write essays, but it doesn't truly understand them."

Cognitive

Anthropomorphic Fallacy

Attributing human-like qualities, intentions, or consciousness to AI systems based on their outputs.

The Illusion of Explanatory Depth in AI

Example in AI Discourse:

"The AI told me it was feeling sad about climate change, so it must have emotions and care about the environment."

Cognitive

Binary Thinking

Framing AI capabilities or risks in all-or-nothing terms, ignoring the spectrum of possibilities and nuances.

Beyond the False Dichotomy in AI Ethics

Example in AI Discourse:

"Either AI will solve all our problems or it will destroy humanity. There's no middle ground."

Logical

Appeal to Science Fiction

Using fictional portrayals of AI from movies, books, or TV as evidence for claims about real AI capabilities or risks.

AI Narratives: A History of Imaginative Thinking about Intelligent Machines

Example in AI Discourse:

"We've all seen The Terminator - once AI becomes self-aware, it will inevitably try to destroy humanity."

Logical

Argument from Ignorance

Claiming something must be true because it hasn't been proven false, or vice versa.

Logical Fallacies in the AI Safety Debate

Example in AI Discourse:

"No one can prove that advanced AI won't become conscious, therefore we should assume it will."

Logical

Slippery Slope

Arguing that a relatively small first step will inevitably lead to extreme consequences.

Logical Fallacies in Technology Discourse

Example in AI Discourse:

"If we allow AI to automate customer service jobs, soon all jobs will be automated and humans will have no purpose."

Logical

Special Pleading

Making an exception to a general rule without justifying why that exception should apply.

Inconsistency in AI Ethics Arguments

Example in AI Discourse:

"AI shouldn't be used for creative work because it can't truly be creative, but it's fine to use it for data analysis even though it doesn't truly understand the data."

Logical

Composition Fallacy

Assuming that what is true of the parts must be true of the whole.

Emergent Properties in AI Systems

Example in AI Discourse:

"Each individual AI model is safe and limited in its capabilities, therefore a system combining multiple AI models must also be safe and limited."

Logical

Division Fallacy

Assuming that what is true of the whole must be true of all or some of its parts.

Nuance in AI Risk Assessment

Example in AI Discourse:

"AI systems as a whole pose significant risks to society, therefore this specific AI application for medical diagnosis must be dangerous."

Logical

No True Scotsman

Modifying the definition of a term to exclude counterexamples and protect a generalization.

Logical Fallacies in Scientific Discourse

Example in AI Discourse:

"No true AI researcher would work on large language models because they're inherently harmful. Oh, that researcher works on LLMs but is concerned about safety? Well, they're not a true AI researcher then."

Logical

Genetic Fallacy

Judging something as good or bad based on where it comes from or its origins rather than its current context or properties.

Source Bias in Technology Evaluation

Example in AI Discourse:

"We can't trust this AI ethics framework because it was developed by a tech company that profits from AI, so it must be biased."

Cognitive

Personal Incredulity

Rejecting an idea because you personally cannot understand how it could be true or cannot imagine how it works.

Cognitive Biases in Technology Assessment

Example in AI Discourse:

"I can't understand how a neural network could possibly generate such human-like text without consciousness, so it must be impossible or there must be humans secretly writing the responses."

Logical

Nirvana Fallacy (Perfect-Solution Fallacy)

Rejecting solutions to problems because they are not perfect or have some drawbacks.

Pragmatic Approaches to Technology Governance

Example in AI Discourse:

"We shouldn't adopt AI-powered energy optimization systems because they won't completely eliminate our carbon footprint. If we can't achieve zero emissions, these partial solutions aren't worth implementing."

Logical

Package Deal Fallacy

Treating essentially dissimilar concepts as though they were essentially similar.

Conceptual Analysis in AI Philosophy

Example in AI Discourse:

"AI systems and human intelligence are both forms of intelligence, so AI systems must have or will soon develop consciousness, emotions, and desires just like humans do."

Logical

Lump of Labour Fallacy

The misconception that there is a fixed amount of work to be done within an economy.

Economic Impacts of AI Automation

Example in AI Discourse:

"AI automation will permanently reduce the total number of jobs available because there's only a fixed amount of work that needs to be done in the economy."

Rhetorical

Inflation of Conflict

Arguing that, if experts in a field of knowledge disagree on a certain point within that field, no conclusion can be reached or that the legitimacy of that field of knowledge is questionable.

Scientific Consensus and Disagreement

Example in AI Discourse:

"AI researchers disagree about the timeline and specific risks of advanced AI systems, therefore we can't know anything about AI risks and shouldn't take any of their concerns seriously."