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

arXiv: https://arxiv.org/abs/2010.13266 · HTML: https://mlsanity.com/paper/2010.13266/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Inspecting the Geographical Representativeness of Images from Text-to-Image Models](https://mlsanity.com/paper/2305.11080/) (2023)
- [Improving Diffusion-Based Image Synthesis with Context Prediction](https://mlsanity.com/paper/2401.02015/) (2024)
- [Synthetic Data in AI: Challenges, Applications, and Ethical Implications](https://mlsanity.com/paper/2401.01629/) (2024)
- [Explaining generative diffusion models via visual analysis for interpretable decision-making process](https://mlsanity.com/paper/2402.10404/) (2024)
- [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://mlsanity.com/paper/2302.10916/) (2023)
- [The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases](https://mlsanity.com/paper/2401.10535/) (2024)
- [Ethics and Creativity in Computer Vision](https://mlsanity.com/paper/2112.03111/) (2021)
- [Is AI Art Another Industrial Revolution in the Making?](https://mlsanity.com/paper/2301.05133/) (2023)
- [Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older Adults](https://mlsanity.com/paper/2403.01413/) (2024)
- [DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model](https://mlsanity.com/paper/2501.18642/) (2025)
- [Building Bridges: Generative Artworks to Explore AI Ethics](https://mlsanity.com/paper/2106.13901/) (2021)
- [Generative AI May Prefer to Present National-level Characteristics of Cities Based on Stereotypical Geographic Impressions at the Continental Level](https://mlsanity.com/paper/2310.04897/) (2023)
- [Detecting Spurious Correlations via Robust Visual Concepts in Real and AI-Generated Image Classification](https://mlsanity.com/paper/2311.01655/) (2023)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems](https://mlsanity.com/paper/2301.01914/) (2023)
- [ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality](https://mlsanity.com/paper/2402.18797/) (2024)
- [Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity](https://mlsanity.com/paper/2412.14077/) (2024)
- [Where is my Glass Slipper? AI, Poetry and Art](https://mlsanity.com/paper/2503.05781/) (2025)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [Disentangling Algorithmic Bias from Archival Artifacts: A Controlled Audit of Vision-Language Model Valuation in Metropolitan Museum Archives](https://mlsanity.com/paper/2609.17572/) (2026)

## Abstract (arXiv)

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.