Related papers: Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content

Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content

URL: http://arxiv.org/abs/2303.06217v2
Date: Tue, 25 Jun 2024 14:44:50 GMT
Title: Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content
Authors: Manav Raj, Justin Berg, Rob Seamans,
Abstract summary: We show that disclosure regarding the use of AI in the creation of creative content affects human evaluation of such content. We interpret this result to suggest that reactions to AI-generated content may be negative when the content is viewed as distinctly "human"
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The emergence of generative AI technologies, such as OpenAI's ChatGPT chatbot, has expanded the scope of tasks that AI tools can accomplish and enabled AI-generated creative content. In this study, we explore how disclosure regarding the use of AI in the creation of creative content affects human evaluation of such content. In a series of pre-registered experimental studies, we show that AI disclosure has no meaningful effect on evaluation either for creative or descriptive short stories, but that AI disclosure has a negative effect on evaluations for emotionally evocative poems written in the first person. We interpret this result to suggest that reactions to AI-generated content may be negative when the content is viewed as distinctly "human." We discuss the implications of this work and outline planned pathways of research to better understand whether and when AI disclosure may affect the evaluation of creative content.

Related papers

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)
Can AI Enhance its Creativity to Beat Humans ? [0.0]
This study investigates the creative performance of artificial intelligence (AI) compared to humans. Human external evaluators have scored creative outputs generated by humans and AI. Results suggest that integrating human feedback is crucial for maximizing AI's creative potential.
arXiv Detail & Related papers (2024-09-27T14:19:07Z)
Measuring Human Contribution in AI-Assisted Content Generation [68.03658922067487]
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.
arXiv Detail & Related papers (2024-08-27T05:56:04Z)
Can AI Be as Creative as Humans? [84.43873277557852]
We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
arXiv Detail & Related papers (2024-01-03T08:49:12Z)
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 and the creative realm: A short review of current and future applications [2.1320960069210484]
This study explores the concept of creativity and artificial intelligence (AI) The development of more sophisticated AI models and the proliferation of human-computer interaction tools have opened up new possibilities for AI in artistic creation.
arXiv Detail & Related papers (2023-06-01T12:28:08Z)
A Portrait of Emotion: Empowering Self-Expression through AI-Generated Art [0.0]
We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. Results show a preference for images based on the descriptions of the authors' emotions over the main events. Our research framework with generative AIs can help design AI-based interventions in related fields.
arXiv Detail & Related papers (2023-04-26T06:54:53Z)
Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI [8.379286663107845]
Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. This paper presents the results of a qualitative survey investigating how creative professionals think about generative AI.
arXiv Detail & Related papers (2023-03-15T20:57:03Z)
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)
Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z)

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