This form of fallacy suggests that allowing one event to happen will inevitably lead to a series of generally negative consequences, without proper evidence that these events are connected.

In analyzing AI-generated content and its copyrightability it can be easy to exaggerate the potential consequences it might cause especially from a legal perspective. It is easy to fall into the "worst case scenario" mindset rather than sticking to facts or precedent despite emotions tied to the factual matter.

Example:

"If we allow AI-generated art to be copyrighted, it won't be long before human artists are completely replaced and original creativity will be lost forever."

Rewritten Approach:

"While recognizing AI-generated content under copyright law raises important concerns about the role of human authorship, this shift does not necessarily threaten the future of human creativity; instead, it invites a reevaluation of how authorship is defined from a legal, technological, and artistic landscape."

From Purdue OWL: "a conclusion based on the premise that if A happens, then eventually through a series of small steps, through B, C,..., X, Y, Z will happen, too, basically equating A and Z. So, if we don't want Z to occur, A must not be allowed to occur either."

In my experience, this is best identified in "if" statements whose consequences transform into certainties (i.e. "IF trans women are allowed into womens sports, then men WILL transition to take advantage of women and WILL win everything.") These fallacies treat undesirable courses of action as inevitabilities and ignore all other potential sequences of events, generally to push a narrative against a specific policy or action that may not actually be harmful.

This occurs frequently in terms of AI discussion. Many raise concerns of AI taking an inevitable path to dominance if AI reaches a certain point of capability. (ex: if AI replaces all tech jobs, we will fall prey to our robot overlords; as hyperbole). A more realistic scenario is the claim made that AI must take over certain roles because of how "advanced" it is proposed to be, despite the actual reality. While AI has advanced and taken on certain roles in business already, its practical and effective usecases remain fairly static.

Conversely, many claim that government restriction of AI and its access to data is a dangerous restriction of power. Some actors have taken to targeting IP law as a restrictor to AI's growth and a stifling of speech.

FALLACY EXAMPLE: If the government rules that AI cannot use any copyrightable work to train, AI will fail entirely and our freedom of speech will be at heavy risk.

this logical fallacy plays on the consumer's emotions. "One's emotions, urges or 'feelings' are innate and in every case self-validating, autonomous, and above any human intent or act of will (one's own or others'), and are thus immune to challenge or criticism." Basically, if you feel a specific emotion while consuming a form of media, it is true.

Affective Fallacy in my paper:

I have been researching how romantic relationships are portrayed in rom coms directed by men vs women. Film is subjective; meaning people will have different opinions on how the whether the film was good or not. This fallacy could be present in my paper by letting my own opinions dictate how the relationships are portrayed, rather than relying on what is actually is shown in the film.

Example:

"Mark Darcy and Daniel are not good romantic options for Bridget Jones because neither of them treat. her with the respect she deserves."

Rewrite:

"While some audience members may argue that neither Mark Darcy or Daniel are not a good romantic match for Bridget, we are able to see the differences in how each pursues her very clearly. There are clear differences in the men's romantic aspirations shown in how they treat Bridget."

Post Hoc. This type of fallacy occurs when one assume that just because something happened after something else doesn't mean the first thing caused the second.

This fallacy could sneak into my analysis when interpreting patterns across different time periods. For example, if media framing of Latino and Hispanic immigrants becomes more negative during the Trump presidency and public sentiment also becomes more hostile, I might too quickly assume that media coverage caused the shift in public opinion even if there could be other variables that could be influencing public perception.

Example:

"Because media coverage of Latino immigrants became more negative during Trump's presidency, this directly caused an increase in anti-immigrant sentiment among the American public."

Rewritten Approach:

"The media's increasingly negative framing of Latino immigrants during Trump's presidency coincided with a rise in anti-immigrant sentiment; while this correlation suggests a potential influence, further research is necessary to determine the extent to which media narratives directly shaped public attitudes."

 Oversimplification in Medical Advice: In many TikTok comments and videos, women share experiences of being told by doctors that the best way to manage their symptoms is simply to lose weight. However, these doctors often fail to provide any guidance, support, or actionable steps—reducing a multifaceted issue to a vague directive: “Just do it.”

Example: “When attempting to lose weight with PCOS, doctor’s advice is usually to lose weight to fix their symptoms.”

Revised: “When attempting to manage weight with PCOS, patients are often advised to lose weight to help alleviate symptoms — however, this can be particularly challenging due to the condition itself, which can make weight loss more difficult.”

This fallacy tries to persuade by appealing to popular opinion or shared identity, rather than logic or evidence.

This fallacy could appear in my study because many announcers have been using player's country of origin, the US or England, as reason for why they are the crowds favorite not logical reasons.

Example:

Everyone's rooting for Jessica Paolini because she's from the United States.

Revised:

The crowd supports Paolini after strong performances and consistency throughout the match.

This fallacy is a conclusion where an argument is reduced to only two choices often with the words "either" and "or".

Example:

"If we want to make campus safer, we can either install more security cameras or ban visitors"

Rewrite:

"If we want to make campus safer. we can install more security cameras around the campus, but due to the high cost of large surveillance, a cheaper and effective option could be limiting visitors after hours."

Assuming something is good simply because it is natural and bad simply because it is unnatural. This oversimplifies issues by reducing them to being either natural or unnatural. An example that disproves this fallacy is glasses. Some people would naturally not be able to see well without the use of glasses. Glasses however are not bad simply because they are unnatural. 

This fallacy could creep into my analysis by me being overly skeptical of the impact of GLP-1’s because they are unnatural. I could be biased towards weight loss options that do not require medication but instead more natural methods like exercise and diet. 

“Big Pharma is pushing these weight loss products which cause unnatural effects in users which are dangerous.” 

Rewritten

“It is important to responsibly evaluate the effects of alternative weight loss methods such as GLP-1’s”

When someone is attacking the character of someone rather than the core of their argument.

2. "The college aged is mail is more likely to struggle with gambling addiction.

I think this fallacy could sneak into my paper by me seeming accusatory towards gamblers rather than addressing the systemic problems.

3. "Gambling companies specifically target college aged males, profiting off their addiction".

Logical Fallacy Identified: The A Priori Argument

Begins with a fixed conclusion or belief and then works backward to find arguments or "evidence" that support that conclusion—regardless of whether the evidence genuinely leads there. Skews reasoning in favor of the predetermined idea. This could sneak into my analysis because of human error or bias when trying to answer my research question.

The researcher went into the landscape with connections that worked at CocaCola. This could skew the analysis due to bias.