Ai Detectors are Bunk!

Prof C
12 Sept 202307:02

TLDRProf C argues that AI detectors for identifying plagiarism are unreliable and should not be used. He explains that AI-generated text cannot be easily tagged and tags can be removed, leading to false positives and negatives. Instead, he suggests focusing on epistemology and creating assignments that require personal experience, making it difficult for AI to assist, and promoting genuine student learning.

Takeaways

  • 🚫 AI Detectors are considered unreliable and should not be used for detecting AI-generated plagiarism.
  • 🖼 AI-generated images can be tagged or watermarked, but text generated by AI cannot be easily tagged without the risk of tag removal.
  • 🔍 Companies' AI detection tools often produce false positives and negatives, making them inaccurate.
  • 📉 Traditional plagiarism tools offer direct sourcing, unlike AI detectors that only provide probability estimates.
  • 🎓 There are instances of students being falsely accused of cheating due to the unreliability of AI detection tools.
  • 📚 AI detection tools are often not open-sourced, making it difficult to understand their inner workings.
  • 📈 Rapid advancements in AI mean that detection systems that work today may not be effective in the future.
  • 🔑 AI generation tools like ChatGPT and Mid-Journey are only going to improve, making them harder to detect.
  • 💡 There are many methods to fool AI detectors, some of which are shared on platforms like YouTube.
  • 🏫 The AI detection arms race is counterproductive and should be stopped, as it pits institutional goals against student goals.
  • 📝 Faculty should consider assigning tasks that require a posteriori knowledge, which is harder for AI to replicate and more engaging for students.

Q & A

  • What is the main argument of the video 'Ai Detectors are Bunk!'?

    -The main argument is that AI detectors are unreliable and should not be used to detect AI-generated plagiarism. They produce many false positives and negatives and cannot be trusted.

  • Why does the professor claim that AI detectors are not effective?

    -AI detectors are not effective because they offer only rough probability estimates and cannot directly source how plagiarism occurred. They also have numerous cases of false accusations and are often used without proper understanding.

  • What did OpenAI do in early 2023 regarding AI detectors?

    -In early 2023, OpenAI, the makers of ChatGPT, released their own AI detector.

  • What is the current state of AI detectors according to the video?

    -According to the video, AI detectors have not proven to reliably distinguish between AI-generated and human-generated content.

  • How does the professor suggest that AI is changing rapidly?

    -The professor suggests that AI is changing rapidly by stating that systems that might have worked well at one time will likely not work in the future, and that AI generation tools will only get better.

  • What is the 'AI detection arms race' mentioned in the video?

    -The 'AI detection arms race' refers to the ongoing struggle between institutions trying to detect AI-generated work and students trying to create AI-generated work that is undetectable.

  • What alternative does the professor propose to the use of AI detectors?

    -The professor proposes creating assignments that require a posteriori knowledge, which depends on experience and is harder for AI to replicate.

  • Why are assignments that require a posteriori knowledge more challenging for AI?

    -Assignments that require a posteriori knowledge are more challenging for AI because they depend on personal experiences and observations that AI cannot predict or generate without actual data or experience.

  • What example does the professor give to illustrate the difference between a priori and a posteriori knowledge in assignments?

    -The professor gives the example of asking students to write an essay about the accomplishments of Harry S Truman versus how their town or family was changed by World War One, where the latter requires personal experience or research.

  • What advice does the professor have for faculty regarding AI in the classroom?

    -The professor advises faculty to pivot their assignments to types that AI cannot easily complete and to teach students how to use AI to assist them in their work, rather than relying on AI detectors.

  • What is the professor's stance on the use of AI in student assignments?

    -The professor believes that AI should be used as a tool to assist students in completing assignments, but not to the extent that it creates verbatim work without personal input or research.

Outlines

00:00

🚫 Unreliable AI Detectors

Prof C discusses the ineffectiveness of AI detectors in identifying AI-generated plagiarism. They argue that these tools are unreliable and should not be used. AI-generated content cannot be easily tagged, and if tagged, the tags can be removed. AI detectors only provide a rough probability estimate without clear sourcing, leading to false accusations of cheating. Prof C also mentions that these tools are often a 'black box' and are not open-sourced, making their inner workings unclear. They reference cases and research that highlight the inadequacy of AI detectors, including an FAQ from OpenAI stating that their detectors are not reliable in distinguishing between AI and human-generated content.

05:01

📚 Rethinking Assignments to Combat Plagiarism

Prof C suggests an alternative approach to assignments to mitigate the issue of AI-generated plagiarism. They propose assigning tasks that require a priori knowledge, which is knowledge that is independent of experience and cannot be easily predicted by AI. They give examples of essay topics that would be difficult for AI to write about, such as personal experiences or local history, because these topics require research and personal experience. Prof C encourages faculty to pivot towards these types of assignments and to teach students how to use AI as a tool to complete them, rather than relying on AI to write their essays. They acknowledge the challenge in changing assignment types and grading methods but argue that it is necessary to end the 'AI detection arms race'.

Mindmap

Keywords

💡AI Detectors

AI Detectors are tools designed to identify content generated by artificial intelligence, such as text or images. In the video, Prof C argues that these detectors are unreliable, often producing false positives and negatives, and should not be trusted. The video's theme revolves around the ineffectiveness of AI detectors in distinguishing between human and AI-generated content.

💡Plagiarism

Plagiarism refers to the act of using another person's work or ideas without giving proper credit, which is a significant concern in academia. The video discusses how AI detectors are being used to combat plagiarism involving AI-generated text, but the effectiveness of these tools is questioned.

💡False Positives and Negatives

False positives occur when a detector incorrectly identifies human-generated content as AI-generated, while false negatives are when it fails to detect AI-generated content. Prof C mentions these issues to highlight the unreliability of AI detectors, leading to wrongful accusations and missed instances of AI-generated content.

💡AI Generation Tools

AI generation tools, like ChatGPT and Mid-Journey, are platforms that use AI to create content. The video suggests that these tools are only going to improve, making it increasingly difficult for detectors to identify AI-generated content accurately.

💡Black Box

A 'black box' refers to a system where the internal processes are not visible to the user. Prof C uses this term to describe AI detectors, indicating that their workings are not transparent, which contributes to the difficulty in trusting their accuracy.

💡Epistemology

Epistemology is the branch of philosophy concerned with the nature and scope of knowledge. The video takes a diversion into epistemology to discuss different types of knowledge and how they relate to assigning and grading essays, suggesting that assignments that require experiential knowledge are less susceptible to AI-generated plagiarism.

💡A Priori Knowledge

A priori knowledge is knowledge that is independent of experience, existing before any empirical evidence. In the context of the video, Prof C explains that when assigning essays with a priori knowledge, the expected outcome is predictable, making it easier for AI to generate content that could pass for human-written.

💡A Posteriori Knowledge

A posteriori knowledge is knowledge that depends on experience and is obtained through observation or experimentation. The video suggests that assignments requiring a posteriori knowledge are more challenging for AI to complete, as they require unique experiences or observations that AI cannot replicate.

💡Harry S Truman

Harry S Truman is used as an example in the video to illustrate the difference between a priori and a posteriori knowledge. While AI might predict an essay on Truman's accomplishments, an essay about the personal impact of World War One on a student's town would require a posteriori knowledge, making it harder for AI to generate.

💡Assignments

Assignments are tasks given to students by educators. The video discusses the need to change the nature of assignments to ones that require personal experience or research, which are less likely to be completed by AI, thus avoiding the reliance on AI detectors.

Highlights

AI detectors are considered unreliable and should not be used.

AI-generated images can be tagged or watermarked, but text cannot be easily tagged as AI-generated.

AI detection tools often produce false positives and negatives, unlike traditional plagiarism tools.

AI detection tools offer only a rough probability estimate without direct sourcing.

There are cases of students being falsely accused of cheating due to AI detection tools.

AI detection tools can be a 'black box' with no open-sourced versions available for transparency.

OpenAI, creators of ChatGPT, admit AI content detectors are not reliable.

AI is evolving rapidly, making current detection systems potentially obsolete in the future.

AI generation tools are at their worst now and will only improve.

There are many systems that can generate undetectable AI text.

Prompts can be modified to fool AI detectors, as shown in various online tutorials.

The AI detection arms race is counterproductive for both institutions and students.

Epistemology provides a framework for rethinking assignments to avoid AI plagiarism.

A priori knowledge assignments are easy for AI to complete, while a posteriori knowledge assignments are not.

Assigning a posteriori knowledge tasks can prevent AI-generated plagiarism.

Faculty should consider teaching students how to use AI ethically for assignments.

Transitioning to new assignment types and grading methods is challenging but necessary.

Ending the AI detection arms race is crucial as current detectors are ineffective.