Biases in Generative Art -- A Causal Look from the Lens of Art History

• URL: http://arxiv.org/abs/2010.13266v2
• Date: Tue, 16 Feb 2021 19:01:11 GMT
• Title: Biases in Generative Art -- A Causal Look from the Lens of Art History
• Authors: Ramya Srinivasan, Kanji Uchino
• Abstract summary: 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.
• Score: 3.198144010381572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With rapid progress in artificial intelligence (AI), popularity of generative art has grown substantially. From creating paintings to generating novel art styles, AI based generative art has showcased a variety of applications. However, there has been little focus concerning the ethical impacts of AI based generative art. In this work, we investigate biases in the generative art AI pipeline right from those that can originate due to improper problem formulation to those related to algorithm design. Viewing from the lens of art history, we discuss the socio-cultural impacts of these biases. Leveraging causal models, we highlight how current methods fall short in modeling the process of art creation and thus contribute to various types of biases. We illustrate the same through case studies, in particular those related to style transfer. To the best of our knowledge, this is the first extensive analysis that investigates biases in the generative art AI pipeline from the perspective of art history. We hope our work sparks interdisciplinary discussions related to accountability of generative art.

Related papers

• YaART: Yet Another ART Rendering Technology [119.09155882164573]
  This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences. We analyze how these choices affect both the efficiency of the training process and the quality of the generated images. We demonstrate that models trained on smaller datasets of higher-quality images can successfully compete with those trained on larger datasets.
  arXiv  Detail & Related papers  (2024-04-08T16:51:19Z)
• AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art [36.21731898719347]
  We conduct a comprehensive analysis to position AI-generated art within the context of human art heritage. Our comparative analysis is based on an extensive dataset, dubbed ArtConstellation'' Key finding is that AI-generated artworks are visually related to the principle concepts for modern period art made in 1800-2000.
  arXiv  Detail & Related papers  (2024-02-04T11:49:51Z)
• Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art [0.0]
  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.
  arXiv  Detail & Related papers  (2024-01-27T20:22:46Z)
• State of the Art on Diffusion Models for Visual Computing [191.6168813012954]
  This report introduces the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model. We also give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing. We discuss available datasets, metrics, open challenges, and social implications.
  arXiv  Detail & Related papers  (2023-10-11T05:32:29Z)
• 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)
• DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection [57.51313366337142]
  There has been growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery and data poisoning. We introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection.
  arXiv  Detail & Related papers  (2023-06-02T05:11:27Z)
• 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)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• 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)
• 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)
• Understanding and Creating Art with AI: Review and Outlook [12.614901374282868]
  Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. This paper provides an integrated review of two facets of AI and art: 1) AI is used for art analysis and employed on digitized artwork collections; 2) AI is used for creative purposes and generating novel artworks. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail.
  arXiv  Detail & Related papers  (2021-02-18T01:38:11Z)

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.