# The Work of Art in an Age of Mechanical Generation

arXiv: https://arxiv.org/abs/2101.11587 · HTML: https://mlsanity.com/paper/2101.11587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks](https://mlsanity.com/paper/2011.05552/) (2020)
- [MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs](https://mlsanity.com/paper/2012.02821/) (2020)
- [Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication](https://mlsanity.com/paper/1711.03536/) (2017)
- [Salient Slices: Improved Neural Network Training and Performance with Image Entropy](https://mlsanity.com/paper/1907.12436/) (2019)
- [A Neural Network Looks at Leonardo's(?) Salvator Mundi](https://mlsanity.com/paper/2005.10600/) (2020)

## Abstract (arXiv)

Can we define what it means to be "creative," and if so, can our definition drive artificial intelligence (AI) systems to feats of creativity indistinguishable from human efforts? This mixed question is considered from technological and social perspectives. Beginning with an exploration of the value we attach to authenticity in works of art, the article considers the ability of AI to detect forgeries of renowned paintings and, in so doing, somehow reveal the quiddity of a work of art. We conclude by considering whether evolving technical capability can revise traditional relationships among art, artist, and the market.