Understanding false positives within Turnitin’s AI writing detection capabilities

Turnitin
14 Mar 202303:27

TLDRDavid Adamson from Turnitin discusses the introduction of an AI writing detection feature aimed at identifying AI-generated text in student submissions. The tool prioritizes precision, accepting a lower recall rate to minimize false positives. The false positive rate is about 1%, with potential errors in repetitive or non-prose text. The tool is being fine-tuned for English language learners, with ongoing efforts to ensure fairness and precision.

Takeaways

  • 🔍 Turnitin is introducing an AI writing detector to help instructors understand how students use AI writing tools.
  • 🎯 The detector prioritizes precision over recall, aiming to be confident when identifying AI-written content.
  • 🤖 There's an acceptance of potential false positives, with a lower recall rate to ensure high precision.
  • 📚 The evaluation set includes a diverse range of documents to mimic academic writing and AI writing integration.
  • ✅ The detector sets a high precision target, marking text as AI-written only if it meets the detection score threshold.
  • ❌ False positives are expected, particularly in repetitive writing or non-prose formats like lists and poetry.
  • 👨‍🏫 Instructors are encouraged to consider the detector's predictions with skepticism and make the final judgment.
  • 📉 The false positive rate is slightly higher for secondary level students, affecting middle and high school writing more.
  • 🌐 No bias against English language learners from any country has been observed, but ongoing monitoring is in place.
  • 🔄 Turnitin is committed to transparency, acknowledging potential mistakes and striving for precision and fairness.

Q & A

  • What is Turnitin's AI writing detection tool designed to do?

    -Turnitin's AI writing detection tool is designed to identify instances where AI writing tools have been used by students in their academic work, allowing instructors to engage with and understand how students are beginning to use such tools.

  • Why did Turnitin prioritize precision over recall in their AI detector?

    -Turnitin prioritized precision over recall to ensure that when a document is flagged as containing AI writing, the prediction is highly reliable. They are willing to miss some instances of AI writing to ensure that the flagged cases are accurate.

  • What is the expected false positive rate for the AI detector according to Turnitin?

    -Turnitin expects a false positive rate of about one percent, meaning that for every hundred fully human-written documents, one might incorrectly be flagged as AI-written.

  • How does Turnitin define a false positive in the context of their AI detector?

    -A false positive in the context of Turnitin's AI detector is when a document that was entirely written by a human is incorrectly identified as containing AI-generated content.

  • What types of writing might be incorrectly flagged as AI-written due to repetitiveness?

    -Repetitive writing, where text substantially repeats itself either word for word or closely paraphrasing, might be incorrectly flagged as AI-written, even if it's just redundant.

  • Why might non-prose content like lists or outlines be incorrectly identified as AI-written?

    -Non-prose content such as lists, outlines, short questions, code, or poetry might be incorrectly identified as AI-written because they can have high self-similarity from item to item and do not resemble paragraphs, which can cause the detector to stumble.

  • How does Turnitin ensure that their AI detector is fair to developing writers and English language learners?

    -Turnitin ensures fairness by oversampling writing from developing writers and English language learners in both their training data and evaluation set, and they continue to monitor for any biases.

  • What is the current false positive rate for secondary level writing compared to higher education?

    -The false positive rate is slightly higher for secondary level writing (middle and high school students) than for higher education, but it is still near the one percent target.

  • How does Turnitin plan to address the higher false positive rate for secondary level writing?

    -Turnitin plans to continue working on improving the AI detector's accuracy for secondary level writing to reduce the false positive rate.

  • What steps is Turnitin taking to ensure their AI detector does not exhibit bias against English language learners from any country?

    -Turnitin is keeping a close eye on the performance of their AI detector and is committed to monitoring and addressing any potential biases against English language learners from any country.

  • What is Turnitin's approach to handling mistakes made by their AI detector?

    -Turnitin aims to own their mistakes, understand them, and share this information with users. They are focused on precision and fairness, even if it means potentially missing some instances of AI writing.

Outlines

00:00

🤖 Introduction to Turnitin's AI Writing Sector

David Adamson, an AI scientist at Turnitin and former high school teacher, introduces Turnitin's new AI writing sector aimed at instructors. The goal is to help educators understand how students are using AI writing tools. Turnitin emphasizes the importance of the reliability of their AI detector's predictions, opting for precision over recall. This means that while the detector may miss some AI-written content, it aims to be highly accurate in its detections. The evaluation set used to set the detector's threshold includes a diverse range of documents to mimic academic writing and AI writing. The false positive rate is expected to be around one percent, meaning that about one in a hundred human-written documents might be incorrectly flagged as AI-written.

Mindmap

Keywords

💡AI writing detection

AI writing detection refers to the process of identifying instances where artificial intelligence tools have been used to generate or assist in writing text. In the context of the video, Turnitin's AI scientist discusses the development of a detector to identify AI-generated content within academic submissions. The goal is to ensure academic integrity by distinguishing between original student work and AI-assisted work.

💡Precision

Precision in the context of AI writing detection is the measure of how many of the documents flagged as AI-written are actually AI-written. The video emphasizes that Turnitin prioritizes precision over recall, meaning they aim to minimize false positives (instances where human-written documents are incorrectly identified as AI-written) even if it means missing some AI-written documents.

💡Recall

Recall is the measure of how many of the actual AI-written documents are correctly identified by the AI detector. The video mentions that by prioritizing precision, Turnitin may have a lower recall rate, which means they might miss some AI-written documents but are confident in the accuracy of the detections they do make.

💡False positives

A false positive in AI writing detection occurs when a document that was entirely written by a human is incorrectly identified as being AI-written. The video states that Turnitin's detector has a false positive rate of about one percent, meaning for every hundred human-written documents, one might be mistakenly flagged as AI-written.

💡Repetitive writing

Repetitive writing is a style of writing where the text substantially repeats itself, either verbatim or through close paraphrasing. The video explains that such writing might be mistakenly identified as AI-written by Turnitin's detector, even if it's just redundant human writing.

💡English language prose

English language prose refers to written language that is not in the form of poetry or other structured forms but is instead composed of paragraphs and sentences. The video clarifies that Turnitin's AI writing detector is designed for prose and may not perform well on other forms of writing, such as lists, outlines, or poetry.

💡Self-similarity

Self-similarity in the context of the video refers to the characteristic of certain types of writing, such as lists or outlines, where each item or entry is similar to the others. This can cause the AI detector to falsely identify such documents as AI-written due to the repetitive nature of the content.

💡Developing writers

Developing writers are individuals who are still learning and improving their writing skills. The video acknowledges that the writing of developing writers, particularly English language learners, might be more redundant and could lead to a higher false positive rate for Turnitin's AI detector.

💡English language learners

English language learners are individuals who are not native speakers of English and are in the process of learning the language. The video discusses the challenge of detecting AI writing in this group, as their writing might naturally contain more repetition, which could be mistaken for AI-assisted writing.

💡Bias

Bias in AI systems refers to the unfair treatment or discrimination against certain groups, which can manifest in the system's predictions or outputs. The video assures that Turnitin is vigilant about ensuring that their AI writing detector does not exhibit bias against English language learners from any country or educational level.

💡Threshold

A threshold in the context of AI writing detection is the minimum score a document must achieve to be considered AI-written by the detector. The video explains that Turnitin sets a high threshold to ensure precision, meaning only documents that strongly indicate AI involvement are flagged.

Highlights

Turnitin is introducing an AI writing detector for instructors to understand student use of AI writing tools.

The AI detector prioritizes precision over recall, aiming to be confident in detections of AI-written content.

Turnitin is fine with missing some AI writing instances to ensure high precision in detections.

The evaluation set used for the AI detector represents various academic writing styles, including potential AI-generated content.

Documents are only flagged as AI-written if they meet a high precision threshold.

The AI detector is more likely to under-predict AI writing rather than over-predict.

The false positive rate for fully human-written documents is about one percent.

Instructors should treat AI predictions with caution and use their judgment for final interpretation.

Repetitive writing may be falsely predicted as AI writing due to its redundancy.

The detector is designed for English prose and may struggle with lists, outlines, short questions, code, or poetry.

The false positive rate is slightly higher for secondary level writing compared to higher education.

Turnitin has not found evidence of bias against English language learners in its AI detector.

Turnitin is committed to owning mistakes and improving the AI detector for precision and fairness.

The AI detector is in development and will be closely monitored for accuracy and bias.

Turnitin encourages transparency and understanding of the AI detector's limitations.

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