Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art

• URL: http://arxiv.org/abs/2401.15497v4
• Date: Tue, 14 May 2024 23:09:13 GMT
• Title: Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art
• Authors: Juniper Lovato, Julia Zimmerman, Isabelle Smith, Peter Dodds, Jennifer Karson,
• Abstract summary: Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. We surveyed 459 artists to investigate tension between artists' opinions on Generative AI art's potential utility and harm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.

Related papers

• Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI [61.35083814817094]
  Several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. We find that low-effort and "off-the-shelf" techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI.
  arXiv  Detail & Related papers  (2024-06-17T18:51:45Z)
• 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)
• Studying Artist Sentiments around AI-generated Artwork [25.02527831382343]
  We interviewed 7 artists and analyzed public posts from artists on social media platforms Reddit, Twitter and Artstation. We report artists' main concerns and hopes around AI-generated artwork, informing a way forward for inclusive development of these tools.
  arXiv  Detail & Related papers  (2023-11-22T22:44:02Z)
• 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)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• Redefining Relationships in Music [55.478320310047785]
  We argue that AI tools will fundamentally reshape our music culture. People working in this space could decrease the possible negative impacts on the practice, consumption and meaning of music.
  arXiv  Detail & Related papers  (2022-12-13T19:44:32Z)
• Can There be Art Without an Artist? [1.2691047660244335]
  Generative AI based art has proliferated in the past year. In this paper, we explore how Generative Models have impacted artistry. We posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art.
  arXiv  Detail & Related papers  (2022-09-16T01:23:19Z)
• Pathway to Future Symbiotic Creativity [76.20798455931603]
  We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
  arXiv  Detail & Related papers  (2022-08-18T15:12:02Z)
• 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)
• Quantifying Confounding Bias in Generative Art: A Case Study [3.198144010381572]
  We propose a simple metric to quantify confounding bias due to the lack of modeling the influence of art movements in learning artists' styles. The proposed metric is more effective than state-of-the-art outlier detection method in understanding the influence of art movements in artworks.
  arXiv  Detail & Related papers  (2021-02-23T21:59:30Z)
• Biases in Generative Art -- A Causal Look from the Lens of Art History [3.198144010381572]
  We investigate biases in the generative art AI pipeline from those that can originate due to improper problem formulation to those related to algorithm design. We highlight how current methods fall short in modeling the process of art creation and thus contribute to various types of biases.
  arXiv  Detail & Related papers  (2020-10-26T00:49:09Z)

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.