Understanding false positives within Turnitin’s AI writing detection capabilities

Turnitin
14 Mar 202303:27

TLDRDavid Adamson from Turnitin discusses their new AI writing detector aimed at helping instructors identify AI-generated content. The tool prioritizes precision over recall, accepting a lower detection rate to minimize false positives. The detector is designed to analyze academic writing and may misinterpret repetitive or non-prose text. False positive rates are about one percent, slightly higher for secondary level students. Turnitin is committed to addressing these issues and ensuring fairness, urging instructors to consider their predictions with caution.

Takeaways

  • 🔍 Turnitin is introducing an AI writing detection feature to help instructors understand how students are using AI writing tools.
  • 🎯 The AI detector prioritizes precision over recall, aiming to be confident in identifying AI-written content, even if it means missing some instances.
  • 📚 The evaluation set includes a diverse range of documents to mimic academic writing and the potential use of AI writers.
  • 📈 A high precision target is set, meaning only documents with a detection score meeting this threshold are flagged as AI-written.
  • ❌ False positives are expected, with a rate of about one percent for fully human-written documents.
  • 🔄 The detector might incorrectly flag repetitive or redundant writing as AI-generated, even if it's not.
  • 📝 The tool is designed for English prose and may not perform well with lists, outlines, short questions, code, or poetry due to their structure.
  • 🌐 False positive rates are slightly higher for secondary level students and English language learners, though efforts are made to minimize this.
  • 🚫 There is no current evidence of bias against English language learners from any country.
  • 🔄 Turnitin is committed to transparency, acknowledging potential mistakes, and striving for precision and fairness in their AI detection system.

Q & A

  • What is Turnitin's approach to AI writing detection?

    -Turnitin prioritizes precision in its AI writing detection, aiming to be highly confident when it identifies a document as containing AI-generated text.

  • Why might Turnitin's AI detector have a lower recall rate?

    -Turnitin is fine with a lower recall rate because they prioritize precision, meaning they would rather miss some AI-written content than incorrectly flag non-AI-written content.

  • How does Turnitin set the threshold for detecting AI-written text?

    -Turnitin uses a large set of documents representing various academic writing styles and AI writing usage to set a high precision target for its predictions.

  • What is the expected false positive rate for Turnitin's AI writing detector?

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

  • What types of writing can cause Turnitin's detector to incorrectly flag as AI-written?

    -Repetitive writing, such as text that substantially repeats itself or closely paraphrases previous content, may be incorrectly predicted as AI writing due to its redundancy.

  • Why might non-prose submissions like lists or outlines be incorrectly flagged by Turnitin's detector?

    -Submissions that are not prose, such as lists, outlines, or poetry, can have high self-similarity from item to item, which does not resemble typical paragraphs and can cause the detector to stumble.

  • How does Turnitin address the potential for false positives in writing from developing writers or English language learners?

    -Turnitin oversamples writing from developing writers and English language learners in both training and evaluation sets, but acknowledges that the false positive rate is slightly higher for secondary level writing.

  • Is Turnitin's AI writing detector biased against English language learners from any specific country?

    -Turnitin has not seen evidence of bias against English language learners from any country, and they will continue to monitor this closely as they move towards production.

  • What is Turnitin's stance on owning mistakes in their AI writing detection system?

    -Turnitin wants to own their mistakes, understand them, and share how and when they might be wrong, emphasizing precision and fairness in their approach.

  • What is the role of instructors in interpreting Turnitin's AI writing detection results?

    -Instructors are expected to take Turnitin's predictions with a grain of salt and make the final interpretation, considering their knowledge of the student and the context.

  • How does Turnitin plan to improve the accuracy of its AI writing detection for specific groups like secondary level students?

    -Turnitin is working on improving the accuracy of its AI writing detection, especially for secondary level students, by continuing to refine its algorithms and data sets.

Outlines

00:00

🤖 Introduction to Turnitin's AI Writing Detector

David Adamson, an AI scientist at Turnitin and a former high school teacher, introduces Turnitin's AI writing detector. The tool is designed to help instructors understand how students are using AI writing tools. Turnitin prioritizes precision in its detector, aiming to be confident when it identifies AI-written content. This approach might lead to a lower recall rate, meaning some AI-written content might be missed, but the focus is on being more accurate in detections. The evaluation set consists of a diverse range of documents to mimic academic writing and AI writing usage. The detector is set to a high precision target, counting text as AI-written only if it meets the detection score threshold. The false positive rate is expected to be around one percent, which is acceptable but not zero, indicating that the tool's predictions should be taken with caution and instructors should make the final interpretation considering the student and context.

Mindmap

Keywords

💡AI writing detection

AI writing detection refers to the process of identifying whether a piece of text has been generated or significantly influenced by artificial intelligence tools. In the context of the video, Turnitin is developing an AI writing detector to help instructors understand how students might be using AI writing tools in their academic work. The video emphasizes the importance of precision in these detections, aiming to minimize false positives.

💡Precision

Precision in the context of AI writing detection is the measure of how many of the detected instances of AI writing are actually correct. The video explains that Turnitin prioritizes precision over recall, meaning they aim to be very sure when they flag a document as containing AI-written content, even if this means they might miss some instances of AI writing.

💡Recall

Recall is the measure of how many of the actual instances of AI writing are correctly identified by the detector. The video acknowledges that by prioritizing precision, the recall rate might be lower, meaning some AI-written content might not be detected. This is a trade-off Turnitin is willing to make to ensure the reliability of their detection system.

💡False positive rate

A false positive rate in AI writing detection is the proportion of human-written documents that are incorrectly flagged as AI-written. The video states that Turnitin expects a false positive rate of about one percent, which means that for every hundred human-written documents, one might be mistakenly identified 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 suggests that such writing might be mistakenly identified as AI-written by Turnitin's detector, even if it is not.

💡English language prose

English language prose refers to written language that is not in the form of poetry or lyrics, typically consisting of paragraphs. The video clarifies that Turnitin's AI writing detector is designed for paragraphs of prose and might not perform as well with other forms of writing, such as lists, outlines, or poetry.

💡Self-similarity

Self-similarity in the context of the video refers to the repetition of similar phrases or structures within a text. The detector might flag items with high self-similarity as AI-written, even if they are not, because they do not conform to the typical structure of paragraphs.

💡Developing writers

Developing writers are individuals who are still learning and improving their writing skills. The video mentions that the writing of developing writers, particularly English language learners, might be more redundant and thus more likely to be falsely flagged by the AI writing detector.

💡English language learners

English language learners are individuals who are not native English speakers and are in the process of learning the language. The video discusses that Turnitin is aware that their false positive rate might be slightly higher for secondary level students, which includes many English language learners, compared to higher education students.

💡Bias

Bias in AI systems refers to the unfair or prejudiced treatment of certain groups or types of content. The video assures that Turnitin is vigilant about ensuring their AI writing detector does not exhibit bias against English language learners from any country or at any education level.

💡Production

In the context of the video, 'production' refers to the stage where the AI writing detector will be fully implemented and available for use by Turnitin's users. The video indicates that the company is working towards this stage while closely monitoring the performance and fairness of their detector.

Highlights

Turnitin is introducing an AI writing detector to help instructors understand how students are using AI writing tools.

The detector prioritizes precision over recall, aiming to be confident when identifying AI-written documents.

The evaluation set includes a diverse range of documents to represent various academic writing styles and AI writing integration.

The detector sets a high precision target, potentially leading to under-prediction of AI-written text.

The false positive rate is expected to be around one percent for fully human-written documents.

Instructors are advised to take predictions with a grain of salt and make the final interpretation.

Repetitive writing, even if human-written, may be falsely predicted as AI-generated due to its redundancy.

The detector is designed for English prose paragraphs and may not perform well with lists, outlines, or poetry.

Developing writers and English language learners might have a slightly higher false positive rate due to redundant writing.

Despite oversampling from diverse writing styles, the false positive rate is still near the one percent target.

Turnitin is monitoring for any biases against English language learners from different countries and educational levels.

The company aims for precision and fairness in its AI writing detector, even if it means missing some AI-written content.

Turnitin acknowledges the potential for mistakes and is committed to understanding and sharing when and how they occur.

The AI writing detector is a tool for instructors to engage with, not a definitive judgment on student work.

The detector's performance is continuously monitored and improved upon to ensure accuracy and fairness.

Instructors are encouraged to consider the context and the student's writing history when evaluating AI detector predictions.

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