AI Art and its Impact on Artists

By Harry H. Jiang, Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, A. Hanna, Johnathan Charles Flowers, Timnit Gebru. AAAI/ACM Conference on AI, Ethics, and Society, 2023

329 citations recorded by the source corpus.

The last 3 years have resulted in machine learning (ML)-based image generators with the ability to output consistently higher quality images based on natural language prompts as inputs. As a result, many popular commercial “generative AI Art” products have entered the market, making generative AI an estimated $48B industry [125]. However, many professional artists have spoken up about the harms they have experienced due to the proliferation of large scale image generators trained on image/text pairs from the Internet. In this paper, we review some of these harms which include reputational damage, economic loss, plagiarism and copyright infringement. To guard against these issues while reaping the potential benefits of image generators, we provide recommendations such as regulation that forces organizations to disclose their training data, and tools that help artists prevent using their content as training data without their consent.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Health Informatics

View Paper: https://www.semanticscholar.org/paper/7dbd3d51c453caf77cc1f9681f9b888095b97443

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Yanzhu Liu
  2. Adams Wai-Kin Kong
  3. Leyang Li
  4. Zilan Wang
  5. Shilin Lu

In Computer Vision and Pattern Recognition

  1. Yanzhu Liu
  2. Adams Wai-Kin Kong
  3. Leyang Li
  4. Zilan Wang
  5. Shilin Lu

Citations in the corpus