AI Content Detection refers to the use of artificial intelligence technologies to identify, analyze, and classify content in various forms—such as text, images, videos, and audio. This process involves algorithms and models designed to recognize patterns, anomalies, and specific attributes within the content.

AI content detection is applied in several areas, including:

  1. Plagiarism Detection: Identifying copied or closely paraphrased content from existing sources to maintain originality and integrity in academic, professional, and creative works.
  2. Fake News and Misinformation: Detecting false or misleading information by analyzing content structure, sources, and context to prevent the spread of misinformation.
  3. Spam Detection: Filtering out unwanted or harmful content in emails, social media, and other digital platforms to protect users from scams and phishing attempts.
  4. Sentiment Analysis: Assessing the emotional tone and sentiment expressed in text to understand public opinion, customer feedback, or social media interactions.
  5. Content Moderation: Automatically flagging and removing inappropriate, offensive, or harmful content in online communities, ensuring a safe and respectful environment.
  6. Deepfake Detection: Identifying manipulated media, such as videos or images, that have been altered using AI techniques to create misleading or fake representations.
  7. Copyright Infringement: Recognizing unauthorized use of copyrighted material to protect intellectual property rights.
  8. Ad Quality and Relevance: Ensuring that advertisements meet quality standards and are relevant to the target audience by analyzing content and context.
  9. Brand Safety: Monitoring and assessing content to ensure it aligns with brand values and avoids association with harmful or controversial material.

 

Posts

Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

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We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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