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About 10,000 papers for “AI-generated content”

AI-generated Content

Feng Zhao, Duoqian Miao · Communications in computer and information science · 2023

Cited by 42Open access

Detecting AI generated content

Dai, Zhongkai · QRU Quaderns de Recerca en Urbanisme · 2024

Nestlé Global Services Spain S.L. commissioned this TFM with the aim of obtaining a tool capable of identifying AI generated text. The project has a clear and ambitious objective: to obtain a model that is capable of improving the detection of cybersecurity threats through a better detection of text generated by AI. This model can later be deployed and used for the automatic de- tection of AI generated phishing emails inside the Nestlé organization. This endeavor encapsulates the essence of contemporary challenges in computational linguistics and natural language processing, addressing the increasingly blurred boundaries between human and machine-generated content in the digital landscape.

Cited by 0Open access

Detecting AI-generated Content

Gerard de Melo · Oxford University Press eBooks · 2025

Abstract The ubiquity of increasingly capable forms of Generative AI has led to a proliferation of AI-generated content in social media, education, entertainment, and elsewhere. While countless beneficial uses of such AI exist, there is also reasonable concern about potential societal risks. There are, hence, legitimate reasons for wishing to distinguish between AI-generated synthetic content and natural content, as written by humans or captured by a camera. This chapter first reviews diverse forms of AI-generated content detection, ranging from heuristic post hoc detectors to custom watermarking schemes, for text, images, and further modalities. The chapter expounds on why all of these suffer from non-negligible limitations. Finally, it outlines a possible path forward to cope with this new world of AI-generated content.

Cited by 0

AI-Generated Content and Copyright

Hind Hammouch, Sushma Singh, Anushka Singh · 2025

Copyright laws safeguard the original work of an author against any unlawful and unlicensed usage, thus playing a vital role in protecting intellectual property rights (IPR). The Copyright Act of 1957 in India provides perpetual and subsequent lifelong and 60-year exclusive rights over the original work. Artificial intelligence (AI) has become highly important in a wide range of technology fields, such as education, healthcare, transportation, aviation, space, and entertainment. In the past, there was no worry about copyright ownership in computer-generated art, as the software was seen as a simple tool that helped with the creative process. Generative AI systems, such as ChatGPT, AIVA, Jukebox, and Google Bard (Gemini), are being employed in several areas, including education, real estate, law enforcement, and Information Technology/Information Technology-Enabled Services (IT/ITeS). This is mostly due to their ability to lower costs, enhance speed, and improve overall efficacy. It is crucial to distinguish between works created through human–AI collaboration and those created solely by AI without human input. This chapter explores the intersection of generative AI and copyright laws, specifically focusing on the convergence in the context of India. Furthermore, it aims to comprehend the complexities and obstacles associated with the issue and offers recommendations on how the legal structure in India might be adjusted to achieve a harmonious equilibrium between promoting AI innovation and protecting the rights of creators.

Cited by 0

More on AI-generated content

Sara Hartley · Current Psychiatry · 2023

In his recent editorial (“A ‘guest editorial’ … generated by ChatGPT?” Current Psychiatry, April 2023, p. 6-7, doi:10.12788/cp.0348), Dr. Nasrallah asked for feedback on the ChatGPT-generated editorial on myths surrounding psychiatry.

Cited by 3Open access

Cite content generated by AI

Biblioteca Universitaria · Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · 2024

Cited by 0

Ethics of AI Generated Content

Romanowski, Alois · Figshare · 2024

As generative AI continues to rapidly advance, it's become difficult to track and understand the moral implications of the technology. Join Librarian Arjay Romanowski as we discuss the ethics of AI generated content, ranging from data bias to cyberreligion.

Cited by 0Open access

Ethics of AI Generated Content

Romanowski, Alois · UR Research (University of Rochester) · 2024

As generative AI continues to rapidly advance, it's become difficult to track and understand the moral implications of the technology. Join Librarian Arjay Romanowski as we discuss the ethics of AI generated content, ranging from data bias to cyberreligion.

Cited by 0Open access

AI Usage Cards: Responsibly Reporting AI-generated Content

Jan Philip Wahle, Terry Ruas, Saif M. Mohammad et al. · 2023

A c c ou ntab il it y "take responsibility" "indicate use" "approve content" Figure 1: A three-dimensional model of responsible AI usage (left) and the header of the AI Usage Cards template (right). ABSTRACT Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern. Although understanding the benefits and harms of using AI systems requires more time, their rapid and indiscriminate adoption in practice is a reality. Currently, we lack a common framework and language to define and report the responsible use of AI for content generation. Prior work proposed guidelines for using AI in specific scenarios (e.g., robotics or medicine) which are not transferable to conducting and reporting scientific research. Our work makes two contributions: First, we propose a three-dimensional model consisting of transparency, integrity, and accountability to define the responsible use of AI. Second, we introduce "AI Usage Cards", a standardized way to report the use of AI in scientific research. Our model and cards allow users to reflect on key principles of responsible AI usage. They also help the research community trace, compare, and question various forms of AI usage and support the development of accepted community norms. The proposed framework and reporting system aim to promote ethical and responsible use of AI in scientific research and provide a standardized approach for reporting AI usage across different research fields. We also provide a free service to easily generate AI Usage Cards for scientific work via a questionnaire and export them in various machine-readable formats for inclusion in different work products at https://ai-cards.org.

Cited by 11Open access

AI Usage Cards: Responsibly Reporting AI-generated Content

Jan Philip Wahle, Terry Ruas, Saif M. Mohammad et al. · arXiv (Cornell University) · 2023

Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern. Although understanding the benefits and harms of using AI systems requires more time, their rapid and indiscriminate adoption in practice is a reality. Currently, we lack a common framework and language to define and report the responsible use of AI for content generation. Prior work proposed guidelines for using AI in specific scenarios (e.g., robotics or medicine) which are not transferable to conducting and reporting scientific research. Our work makes two contributions: First, we propose a three-dimensional model consisting of transparency, integrity, and accountability to define the responsible use of AI. Second, we introduce ``AI Usage Cards'', a standardized way to report the use of AI in scientific research. Our model and cards allow users to reflect on key principles of responsible AI usage. They also help the research community trace, compare, and question various forms of AI usage and support the development of accepted community norms. The proposed framework and reporting system aims to promote the ethical and responsible use of AI in scientific research and provide a standardized approach for reporting AI usage across different research fields. We also provide a free service to easily generate AI Usage Cards for scientific work via a questionnaire and export them in various machine-readable formats for inclusion in different work products at https://ai-cards.org.

Cited by 1Open access

AI Usage Cards: Responsibly Reporting AI-generated Content

Wahle, Jan Philip, Ruas, Terry, Mohammad, Saif M. et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023

Posterpräsentation im Rahmen des 2. Text+ Plenarys: Connecting People and Data am 28./29. September an der SUB Göttingen.

Cited by 0Open access

AI Usage Cards: Responsibly Reporting AI-Generated Content

Jan Philip Wahle, Terry Ruas, Saif M. Mohammad et al. · 2023

There are growing concerns about the responsible use of content-generating AI systems. Current guidelines for using AI are specific to certain scenarios and not applicable to scientific research. We propose a three-dimensional model consisting of transparency, integrity, and accountability to define responsible AI use in science and introduce “AI Usage Cards” to report the use of AI in scientific research. Our model and reporting system promotes the ethical and responsible use of AI and provides a standardized approach for reporting AI across research fields. We also offer a free service to generate AI Usage Cards via a questionnaire at https://ai-cards.org.

Cited by 10

Unknown Aware AI-Generated Content Attribution

Ellie Thieu, Jifan Zhang, Haoyue Bai · arXiv (Cornell University) · 2026

The rapid advancement of photorealistic generative models has made it increasingly important to attribute the origin of synthetic content, moving beyond binary real or fake detection toward identifying the specific model that produced a given image. We study the problem of distinguishing outputs from a target generative model (e.g., OpenAI Dalle 3) from other sources, including real images and images generated by a wide range of alternative models. Using CLIP features and a simple linear classifier, shown to be effective in prior work, we establish a strong baseline for target generator attribution using only limited labeled data from the target model and a small number of known generators. However, this baseline struggles to generalize to harder, unseen, and newly released generators. To address this limitation, we propose a constrained optimization approach that leverages unlabeled wild data, consisting of images collected from the Internet that may include real images, outputs from unknown generators, or even samples from the target model itself. The proposed method encourages wild samples to be classified as non target while explicitly constraining performance on labeled data to remain high. Experimental results show that incorporating wild data substantially improves attribution performance on challenging unseen generators, demonstrating that unlabeled data from the wild can be effectively exploited to enhance AI generated content attribution in open world settings.

Cited by 0Open access

Unknown Aware AI-Generated Content Attribution

Ellie Thieu, Jifan Zhang, Haoyue Bai · ArXiv.org · 2026

The rapid advancement of photorealistic generative models has made it increasingly important to attribute the origin of synthetic content, moving beyond binary real or fake detection toward identifying the specific model that produced a given image. We study the problem of distinguishing outputs from a target generative model (e.g., OpenAI Dalle 3) from other sources, including real images and images generated by a wide range of alternative models. Using CLIP features and a simple linear classifier, shown to be effective in prior work, we establish a strong baseline for target generator attribution using only limited labeled data from the target model and a small number of known generators. However, this baseline struggles to generalize to harder, unseen, and newly released generators. To address this limitation, we propose a constrained optimization approach that leverages unlabeled wild data, consisting of images collected from the Internet that may include real images, outputs from unknown generators, or even samples from the target model itself. The proposed method encourages wild samples to be classified as non target while explicitly constraining performance on labeled data to remain high. Experimental results show that incorporating wild data substantially improves attribution performance on challenging unseen generators, demonstrating that unlabeled data from the wild can be effectively exploited to enhance AI generated content attribution in open world settings.

Cited by 0Open access

Human-Created or AI-Generated Content?

Chihiro Maru, Yuri Hamada, Hiroko Shoji · International Symposium on Affective Science and Engineering · 2025

With advancements in generative artificial intelligence (AI), including large language models (LLMs), AI technologies are being increasingly utilized in various aspects of daily life, and their adoption is expected to proliferate. However, only a few studies have evaluated the content created by generative AI and human responses based on real-world scenarios. Assessing the quality of content involving generative AI and its impact on human psychological acceptance is crucial for appropriate utilization of these technologies. In this study, we evaluated the content created by generative AI for book endorsements. Specifically, the quality of the content created was compared under four generation processes: (i) solely by humans, (ii) solely by ChatGPT, (iii) by ChatGPT and then revised by humans, and (iv) by humans and then revised by ChatGPT. In addition, we investigated how disclosing the content generation process affects evaluation. The results revealed that AI content has a more significant effect on increasing purchase intention than content generated solely by humans. However, when the content-generation process was explained, some resistance toward AI-generated content was observed. This resistance was mitigated when humans were involved in certain aspects of the generation process.

Cited by 0Open access

AI-Generated Content (AIGC): A Survey

Jiayang Wu, Wensheng Gan, Zefeng Chen et al. · arXiv (Cornell University) · 2023

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.

Cited by 87Open access