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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2404.10781 |
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| _version_ | 1866913317039112192 |
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| author | Aburass, Sanad Rumman, Maha Abu |
| author_facet | Aburass, Sanad Rumman, Maha Abu |
| contents | The "Writer's Integrity" framework introduces a paradigm shift in maintaining the sanctity of human-generated text in the realms of academia, research, and publishing. This innovative system circumvents the shortcomings of current AI detection tools by monitoring the writing process, rather than the product, capturing the distinct behavioral footprint of human authorship. Here, we offer a comprehensive examination of the framework, its development, and empirical results. We highlight its potential in revolutionizing the validation of human intellectual work, emphasizing its role in upholding academic integrity and intellectual property rights in the face of sophisticated AI models capable of emulating human-like text. This paper also discusses the implementation considerations, addressing potential user concerns regarding ease of use and privacy, and outlines a business model for tech companies to monetize the framework effectively. Through licensing, partnerships, and subscriptions, companies can cater to universities, publishers, and independent writers, ensuring the preservation of original thought and effort in written content. This framework is open source and available here, https://github.com/sanadv/Integrity.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Authenticity in Authorship: The Writer's Integrity Framework for Verifying Human-Generated Text Aburass, Sanad Rumman, Maha Abu Cryptography and Security The "Writer's Integrity" framework introduces a paradigm shift in maintaining the sanctity of human-generated text in the realms of academia, research, and publishing. This innovative system circumvents the shortcomings of current AI detection tools by monitoring the writing process, rather than the product, capturing the distinct behavioral footprint of human authorship. Here, we offer a comprehensive examination of the framework, its development, and empirical results. We highlight its potential in revolutionizing the validation of human intellectual work, emphasizing its role in upholding academic integrity and intellectual property rights in the face of sophisticated AI models capable of emulating human-like text. This paper also discusses the implementation considerations, addressing potential user concerns regarding ease of use and privacy, and outlines a business model for tech companies to monetize the framework effectively. Through licensing, partnerships, and subscriptions, companies can cater to universities, publishers, and independent writers, ensuring the preservation of original thought and effort in written content. This framework is open source and available here, https://github.com/sanadv/Integrity.github.io |
| title | Authenticity in Authorship: The Writer's Integrity Framework for Verifying Human-Generated Text |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2404.10781 |