Laypeople's Egocentric Perceptions of Copyright for AI-Generated Art

• URL: http://arxiv.org/abs/2407.10546v1
• Date: Mon, 15 Jul 2024 08:53:43 GMT
• Title: Laypeople's Egocentric Perceptions of Copyright for AI-Generated Art
• Authors: Gabriel Lima, Nina Grgić-Hlača, Elissa Redmiles,
• Abstract summary: This research investigates perceptions of AI-generated art concerning factors associated with copyright protection. We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the system to generate the image. Our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills)
• Score: 3.072340427031969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent breakthroughs in generative AI (GenAI) have fueled debates concerning the status of AI-generated creations under copyright law. This research investigates laypeople's perceptions ($N$ = 424) of AI-generated art concerning factors associated with copyright protection. Inspired by prior work suggesting that people show egocentric biases when evaluating their own creative outputs, we also test if the same holds for AI-generated art. Namely, we study the differences between the perceptions of those who have something to gain from copyright protection -- creators of AI-generated art -- and uninvested third parties. To answer our research questions, we held an incentivized AI art competition, in which some participants used a GenAI model to generate images for consideration while others evaluated these submissions. We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the AI system to generate the image and the artists whose creations were used for training the AI model. We also find that participants egocentrically favored their own art over other participants' art and rated their own creations higher than other people evaluated them. Moreover, our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills). Our findings have implications for future debates concerning the potential copyright protection of AI-generated outputs.

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)
• Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? [24.417027069545117]
  Distinguishing AI generated images from human art is a challenging problem. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors.
  arXiv  Detail & Related papers  (2024-02-05T17:25:04Z)
• 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)
• AIxArtist: A First-Person Tale of Interacting with Artificial Intelligence to Escape Creative Block [20.96181205379132]
  The future of the arts and artificial intelligence (AI) is promising as technology advances. This workshop pictorial puts forward first-person research that shares interactions between an HCI researcher and AI. The paper explores two questions: How can AI support artists' creativity, and what does it mean to be explainable in this context.
  arXiv  Detail & Related papers  (2023-08-22T13:15:29Z)
• Artificial Intelligence and Aesthetic Judgment [29.71278672770529]
  Generative AIs produce creative outputs in the style of human expression. We argue that encounters with the outputs of modern generative AI models are mediated by the same kinds of aesthetic judgments.
  arXiv  Detail & Related papers  (2023-08-21T17:40:54Z)
• 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)
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images [66.20578637253831]
  There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
  arXiv  Detail & Related papers  (2023-04-25T17:51:59Z)
• Prote\c{c}\~ao intelectual de obras produzidas por sistemas baseados em intelig\^encia artificial: uma vis\~ao tecnicista sobre o tema [0.0]
  The pervasiveness of Artificial Intelligence (AI) is unquestionable in our society. Even in the arts, AI is present. This essay aims to contribute with a technicist view on the discussion of copyright applicability from works produced by AI.
  arXiv  Detail & Related papers  (2022-05-11T12:07:47Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.