Modern Snake Oil: AI Detectors

What are Text Base AI Detectors?

Text-based AI detectors are software tools designed to identify whether a piece of text was generated by artificial intelligence. These detectors analyze various features of the text, such as sentence structure, word usage, and stylistic patterns, to determine if it matches "typical" AI writing characteristics. 


The basic flaws with AI Detection

The main weakness is that text characteristics cannot reliably indicate authorship. Since you can't validate its findings it's prone to errors, manipulation, and inherent biases. 

The "Unique Indicators" Myth

The Myth is that these AI Detectors, represent a solution to the "AI Problem". The belief that detectors are a solution is based on a myth that assumes AI writing has unique indicators.  The reality is that modern language models show this is not true.

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The illusion of accuracy

While it might seem effective at identifying AI-generated content, it often relies on superficial markers and patterns that can result in both false positives and false negatives. This perceived accuracy can be deceptive, as slight modifications to AI-generated text can easily bypass detection, and genuine human writing will be incorrectly flagged. 


The problem is that there are no clear textual giveaways that reliably distinguish human writing from AI-generated writing. Humans have been writing for over 5,000 years, and there is no particular "style" to train AI on or use which hasn't been used by someone somewhere at some time. As soon as these detectors hit an unknown style or a style it was "taught" to be bad it will go off regardless of when or who wrote it (ex. US Constitution was written by AI).

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In the News:

Ingrained Bias and Discrimination

Text-based AI detectors exhibit bias and discrimination, often rooted in the data they are trained on and the algorithms they use. These systems disproportionately flag certain writing styles or dialects associated with specific cultural or linguistic groups.


False positives are known to impact certain demographics more than others. For example, non-native English speakers are often wrongfully flagged for writing styles that deviate from "standard" English. Students with learning disabilities also suffer from excessive false flags.


The discrimination stems from the use of narrow datasets to train the system.  Without diverse data, they fail to encompass language variations. Detectors offer little to no transparency in their training process or detection criteria. With no public accountability, biases and flaws in proprietary algorithms go unchecked. Those targeted unfairly have no recourse against such an opaque system.

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Unfair Punishment and no way to prove innocence

More and more instances are emerging of educators alleging cheating based on shaky detector evidence alone. 

Examples:


Often you'll hear an argument of "use this as only one piece of evidence" or "Don't make your conclusion solely of this tool."  This is a form of indemnification by the user of the tool or the AI detector software companies, used as an attempt to isolate them from lawsuits.  It is not actual functioning good advice. The fact of the matter is there is no good way to prove your innocence.  Some say to use version history, but this won't work without saving every change you make when writing a document in multiple saved documents. It is a flawed logic;

The result is guilty until proven innocent, with no way to prove your innocence.  Couple this with no recourse to overturn incorrect judgments, and this results in unfair punishment.

OpenAI gives up on detection!

In September 23, Open AI themselves gave up on an AI Detector and issued the following statement:

Do AI detectors work?

The failed effort from the makers of ChatGPT which started all of this hype, further confirms the current challenges with AI Detection. If OpenAI cannot identify synthetic text, this raises significant doubt that any other tool made by another company could reliably detect it from these highly opaque models.

After they saw the unintended consequences of inaccurate AI detection, they decided the harm that occurs outweighed any marginal benefit. This highlights the reality that viable tools do not exist and most likely never will.

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