Related papers: Measuring Human Contribution in AI-Assisted Content Generation

Measuring Human Contribution in AI-Assisted Content Generation

URL: http://arxiv.org/abs/2408.14792v1
Date: Tue, 27 Aug 2024 05:56:04 GMT
Title: Measuring Human Contribution in AI-Assisted Content Generation
Authors: Yueqi Xie, Tao Qi, Jingwei Yi, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu,
Abstract summary: This study raises the research question of measuring human contribution in AI-assisted content generation. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation.
Score: 68.03658922067487
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

Related papers

Unexploited Information Value in Human-AI Collaboration [23.353778024330165]
How to improve performance of a human-AI team is often not clear without knowing what particular information and strategies each agent employs. We propose a model based in statistically decision theory to analyze human-AI collaboration.
arXiv Detail & Related papers (2024-11-03T01:34:45Z)
"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI [55.99010491370177]
We argue that we cannot thoroughly map the social impacts of generative AI without mapping the social impacts of anthropomorphic AI. anthropomorphic AI systems are increasingly prone to generating outputs that are perceived to be human-like.
arXiv Detail & Related papers (2024-10-11T04:57:41Z)
Human Bias in the Face of AI: The Role of Human Judgement in AI Generated Text Evaluation [48.70176791365903]
This study explores how bias shapes the perception of AI versus human generated content. We investigated how human raters respond to labeled and unlabeled content.
arXiv Detail & Related papers (2024-09-29T04:31:45Z)
Decoding AI and Human Authorship: Nuances Revealed Through NLP and Statistical Analysis [0.0]
This research explores the nuanced differences in texts produced by AI and those written by humans. The study investigates various linguistic traits, patterns of creativity, and potential biases inherent in human-written and AI- generated texts.
arXiv Detail & Related papers (2024-07-15T18:09:03Z)
The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence [0.45207442500313766]
I challenge the prevalent narrow conceptualisation of AI as tools, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, and posit that AI can also serve as an instrument for understanding human learning. The paper presents three unique conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems.
arXiv Detail & Related papers (2024-03-24T10:07:46Z)
Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z)
AI empowering research: 10 ways how science can benefit from AI [0.0]
This article explores the transformative impact of artificial intelligence (AI) on scientific research. It highlights ten ways in which AI is revolutionizing the work of scientists, including powerful referencing tools, improved understanding of research problems, enhanced research question generation, optimized research design, stub data generation, data transformation, advanced data analysis, and AI-assisted reporting.
arXiv Detail & Related papers (2023-07-17T18:41:18Z)
Human-AI Coevolution [48.74579595505374]
Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
arXiv Detail & Related papers (2023-06-23T18:10:54Z)
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.