Decoding AI Art

Decoding AI Art

G. Salimbeni’s 2024 thesis, “Decoding AI Art: From Motivation to Manifestation” (University of Nottingham), sits in an interesting intersection between Human–Computer Interaction (HCI) and practice-led art research. Rather than treating AI art as a purely technical phenomenon, it examines it as a human practice shaped by intention, framing, and interpretation.

From what is publicly known and inferred from the abstract you’ve shared, its contribution is less about new AI methods and more about how we understand and evaluate AI art.

Core idea: shifting the focus from “how” to “why”

A central argument of the thesis is that discussions of AI art tend to overemphasise implementation (models, datasets, pipelines) while underexamining motivation. Salimbeni reframes AI art as something driven by:

  • artistic intent
  • conceptual positioning
  • audience interpretation

In this sense, the work aligns with broader HCI traditions that treat technology as embedded in human meaning-making, not just functionality.

The “Five Tropes of AI Art”

One of the thesis’s main contributions is this framework. While the exact taxonomy depends on the full text, the idea is that AI artworks can be analysed through recurring patterns of motivation and narrative, rather than medium or technique.

These “tropes” act as:

  • a curatorial tool (for exhibitions or critique)
  • an analytic lens (for researchers studying AI art practices)

This is quite valuable because AI art is otherwise difficult to categorise using traditional art-historical labels.

Practice-led case study: Cat Royale

The inclusion of Cat Royale grounds the thesis in real artistic production, not just theory.

This case study likely explores:

  • the messy realities of working with AI systems
  • constraints and unpredictability of generative models
  • how artistic intent evolves during the process

Practice-led research in this context is important because it reveals the gap between conceptual ambition and technical execution, something often invisible in purely theoretical work.

Guidelines for analysing AI art

The thesis culminates in a set of analytical guidelines, combining:

  • the Five Tropes framework
  • insights from the case study
  • HCI-informed interpretation

These guidelines aim to help researchers, artists, and curators navigate questions like:

  • What is the role of the artist versus the model?
  • How should authorship be framed?
  • What makes an AI artwork “meaningful” rather than just novel?

Key insight: framing is everything

Perhaps the most important takeaway is the emphasis on framing and stated motivation.

The thesis argues that:

  • AI art is not inherently meaningful because it uses AI
  • meaning emerges from how the work is contextualised
  • weak framing leads to work being perceived as gimmick-driven

This is a subtle but powerful critique of a lot of contemporary AI art, especially pieces that rely on technical novelty alone.

Why it matters

Within HCI and digital art research, this thesis contributes to a broader shift:

  • from technology-centred evaluation → to human-centred interpretation
  • from output aesthetics → to process + intent + narrative

It resonates with ongoing debates about:

  • authorship in generative systems
  • the role of the artist in co-creative AI workflows
  • how audiences assign meaning to algorithmic outputs

If you’re engaging with AI art from a data science or product perspective, there’s an interesting parallel: it’s essentially arguing that models don’t create value on their own; context and interpretation do. That idea translates surprisingly well beyond art.

cite:
@techreport{salimbeni2024decoding,
  title={Decoding AI art: from motivation to manifestation},
  author={Salimbeni, Guido},
  institution={University of Nottingham}
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