Learning to See: You Are What You See
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2021Nine Potential Pitfalls when Designing Human-AI Co-Creative Systemscited by 12
- 2024Visions of Destruction: Exploring a Potential of Generative AI in Interactive Artcited by 1
- 2026Echoes of the Prior: A Computational Phenomenology of Forgettingcited by 0
- 2026World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the Worldcited by 0
- 2026World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the World29 Jun 2026
- 2026Echoes of the Prior: A Computational Phenomenology of Forgetting10 Jun 2026
- 2024Visions of Destruction: Exploring a Potential of Generative AI in Interactive Art26 Aug 2024
- 2021Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems1 Apr 2021