# Learning to See: You Are What You See

arXiv: https://arxiv.org/abs/2003.00902 · HTML: https://mlsanity.com/paper/2003.00902/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems](https://mlsanity.com/paper/2104.00358/) (2021)
- [Visions of Destruction: Exploring a Potential of Generative AI in Interactive Art](https://mlsanity.com/paper/2408.14644/) (2024)
- [Echoes of the Prior: A Computational Phenomenology of Forgetting](https://mlsanity.com/paper/2606.12340/) (2026)
- [World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the World](https://mlsanity.com/paper/2608.07510/) (2026)

## Abstract (arXiv)

The authors present a visual instrument developed as part of the creation of the artwork Learning to See. The artwork explores bias in artificial neural networks and provides mechanisms for the manipulation of specifically trained for real-world representations. The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world. These representations can be explored and manipulated in real time, and have been produced in such a way so as to reflect specific creative perspectives that call into question the relationship between how both artificial neural networks and humans may construct meaning.