How to Use ChatGPT to Detect Logical Fallacies

Have you ever heard or read an argument you instinctively knew was flawed but didn’t know how to refute it? One of the best classes I took in university was a philosophy course in logic. Studying logic taught me how to evaluate the veracity of an argument.

The main technique we learned in this course was how to identify what are called logical fallacies.

The study of logic goes back for 2,400 years to Ancient Greece where Aristotle and Plato first explored the veracity of rhetoric and argumentation.

A logical fallacy is a flaw in reasoning or argument that undermines its validity, often leading to incorrect conclusions. It occurs when the argument relies on faulty logic, irrelevant information, or emotional appeals instead of sound evidence.

Here are the top 10 most commonly used logical fallacies. Over the years, most people have fallen victim to them:

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1: Ad Hominem: Attacking the person making the argument rather than addressing the argument itself.

Example: “You can’t trust her opinion on politics; she’s just a celebrity.”

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2: Strawman: Misrepresenting or oversimplifying someone’s argument to make it easier to attack.

Example: “So you’re saying we should just let criminals go free?”

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3: Appeal to Authority: Using the opinion of an authority figure or celebrity as evidence, even when they’re not an expert on the subject.

Example: “A famous actor supports this diet, so it must work.”

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4: False Dilemma (False Dichotomy): Presenting only two extreme options as the only possibilities, ignoring other alternatives.

Example: “You’re either with us or against us.”

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5: Slippery Slope: Arguing that a small step will inevitably lead to a chain of catastrophic events.

Example: “If we allow one store to open on Sundays, soon every store will be open 24/7.”

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6: Circular Reasoning (Begging the Question): The argument’s conclusion is assumed in its premise, offering no actual support.

Example: “I’m right because I said so.”

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7: Hasty Generalization: Drawing a broad conclusion based on a small or unrepresentative sample.

Example: “My neighbor lost his job, so the economy must be terrible everywhere.”

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8: Red Herring: Introducing irrelevant information to distract from the actual issue.

Example: “Why worry about climate change when we have crime to deal with?”

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9: Appeal to Emotion: Manipulating emotions to win an argument instead of using logical reasoning.

Example: “Think of the children! We must ban this product.”

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10: Post Hoc Ergo Propter Hoc (False Cause): Assuming that because one event followed another, the first caused the second.

Example: “I wore my lucky socks, and we won the game, so the socks caused the victory.”

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These fallacies are common because they can seem persuasive on the surface, even though they don’t hold up under scrutiny.

The point of an argument is to persuade someone. The problem is many people unwittingly use logical fallacies in their argumentation out of habit. Most people have no idea what a logical fallacy is. In the age of X, more people are voicing their opinions, and it’s useful to ascertain what is true in these opinions and what is not.

Here are six typical X tweets that each contain three logical fallacies followed by explanations of what those fallacies are:

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Tweet 1: “Defund the police? If we do that, every city will turn into a crime-ridden wasteland (slippery slope). Police officers are heroes, so how could anyone even question their methods? (appeal to emotion). Only criminals want to defund the police (ad hominem).”

Explanation:

• Slippery slope: Assumes that defunding the police will inevitably lead to extreme negative outcomes.
• Appeal to emotion: Uses the “hero” status of police officers to dismiss legitimate criticism.
• Ad hominem: Attacks those who want to defund the police by calling them criminals instead of addressing their arguments.

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Tweet 2: “If you don’t support free college, you clearly don’t care about the future of young Americans (false dilemma). Why listen to critics of free college when they’re just rich people who can already afford it? (genetic fallacy). Everyone knows free college is the only way to fix the economy (bandwagon).”

Explanation:

• False dilemma: Suggests that caring about young Americans means supporting free college, ignoring other possible solutions.
• Genetic fallacy: Dismisses criticism based on the critics’ wealth rather than the merits of their argument.
• Bandwagon: Assumes that because many people support free college, it must be the right solution.

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Tweet 3: “Gun control laws won’t stop gun violence. Just look at Chicago—strict laws and still tons of crime (cherry picking). The only people who want more gun laws are those who don’t respect the Second Amendment (ad hominem). Either we allow guns, or we live under government tyranny (false dilemma).”

Explanation:

• Cherry picking: Selectively uses Chicago as an example, ignoring other places where gun control has had positive effects.
• Ad hominem: Attacks gun control advocates as being anti-Second Amendment rather than addressing their points.
• False dilemma: Presents only two extreme options: allowing guns or living under tyranny.

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Tweet 4: “Universal healthcare? Next thing you know, the government will be telling us what to eat and how to live (slippery slope). And why listen to people who have never even run a business about healthcare? (ad hominem). Every country with universal healthcare is falling apart (hasty generalization).”

Explanation:

• Slippery slope: Claims universal healthcare will lead to government overreach in unrelated aspects of life.
• Ad hominem: Attacks critics for not being business owners rather than addressing their arguments.
• Hasty generalization: Makes a sweeping negative claim about all countries with universal healthcare based on limited examples.

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Tweet 5: “Raising the minimum wage will obviously destroy small businesses (appeal to fear). If you don’t support raising it, you’re just an enemy of the working class (false dilemma). Studies say it’s harmful, but who trusts studies funded by corporate interests? (genetic fallacy).”

Explanation:

• Appeal to fear: Exaggerates the potential consequences of raising the minimum wage to incite fear.
• False dilemma: Frames the issue as if you either support raising the wage or oppose the working class.
• Genetic fallacy: Dismisses studies based on their funding source rather than analyzing their findings.

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Tweet 6: “Pro-life advocates are all just trying to control women’s bodies (strawman). Either you’re pro-choice, or you don’t believe in women’s rights (false dilemma). Abortion has been around for centuries, so banning it now makes no sense (appeal to tradition).”

Explanation:

• Strawman: Misrepresents the pro-life argument as being solely about controlling women’s bodies, ignoring other nuances.
• False dilemma: Suggests only two options—being pro-choice or being against women’s rights.
• Appeal to tradition: Argues that something should be allowed simply because it has existed for a long time.

Using ChatGPT to Check for Logical Fallacies

Using the example of a sample tweet that supports universal healthcare, here’s exactly how I did this in ChatGPT:

Conclusion

The next time someone tries to pull the wool over their eyes, copy and paste their argument into ChatGPT, Grok, or MSCopilot and ask it to analyze the text for logical fallacies. Using this technique, you can easily vanquish mid-wits and internet trolls.

–Wolfshead

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