# Kaleidoscope Gallery: Exploring Ethics and Generative AI Through Art

arXiv: https://arxiv.org/abs/2505.14758 · HTML: https://mlsanity.com/paper/2505.14758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 97 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 24 of 24)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [Alignment of Language Agents](https://mlsanity.com/paper/2103.14659/) (2021)
- [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023)
- [Word-As-Image for Semantic Typography](https://mlsanity.com/paper/2303.01818/) (2023)
- [Large Language Models Reflect the Ideology of their Creators](https://mlsanity.com/paper/2410.18417/) (2024)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (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)
- [Aligning to Social Norms and Values in Interactive Narratives](https://mlsanity.com/paper/2205.01975/) (2022)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Embedding Democratic Values into Social Media AIs via Societal Objective Functions](https://mlsanity.com/paper/2307.13912/) (2023)
- [Designing for Human Rights in AI](https://mlsanity.com/paper/2005.04949/) (2020)
- [AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks](https://mlsanity.com/paper/2401.06178/) (2024)
- [Trick or TReAT: Thematic Reinforcement for Artistic Typography](https://mlsanity.com/paper/1903.07820/) (2019)
- [Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art](https://mlsanity.com/paper/2401.15497/) (2024)
- [Interpretations, Representations, and Stereotypes of Caste within Text-to-Image Generators](https://mlsanity.com/paper/2408.01590/) (2024)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)
- [Respect as a Lens for the Design of AI Systems](https://mlsanity.com/paper/2206.07555/) (2022)
- [Vernacularizing Taxonomies of Harm is Essential for Operationalizing Holistic AI Safety](https://mlsanity.com/paper/2410.16562/) (2024)
- [Building Bridges: Generative Artworks to Explore AI Ethics](https://mlsanity.com/paper/2106.13901/) (2021)
- [Making Sense of Machine Learning: Integrating Youth's Conceptual, Creative, and Critical Understandings of AI](https://mlsanity.com/paper/2305.02840/) (2023)

## Abstract (arXiv)

Ethical theories and Generative AI (GenAI) models are dynamic concepts subject to continuous evolution. This paper investigates the visualization of ethics through a subset of GenAI models. We expand on the emerging field of Visual Ethics, using art as a form of critical inquiry and the metaphor of a kaleidoscope to invoke moral imagination. Through formative interviews with 10 ethics experts, we first establish a foundation of ethical theories. Our analysis reveals five families of ethical theories, which we then transform into images using the text-to-image (T2I) GenAI model. The resulting imagery, curated as Kaleidoscope Gallery and evaluated by the same experts, revealed eight themes that highlight how morality, society, and learned associations are central to ethical theories. We discuss implications for critically examining T2I models and present cautions and considerations. This work contributes to examining ethical theories as foundational knowledge that interrogates GenAI models as socio-technical systems.