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Design Contradictions: Help or Hindrance?

Aron E. Owen, Jonathan C. Roberts

TL;DR

This paper seeks to start a conversation on how to drive AI systems to be more creative and generate new ideas, and offers practical insights into the potential of AI in driving creativity in data visualisation.

Abstract

The need for innovative ideas in data visualisation drives us to explore new creative approaches. Combining two or more creative words, particularly those that contradict each other, can positively impact the creative process, sparking novel ideas and designs. As we move towards AI-driven design, an open question arises: do these design contradictions work positively with AI tools? Currently, the answer is no. AI systems, like large language models (LLMs), rely on algorithms that engender similarity, whereas creativity often requires divergence and novelty. This poster initiates a conversation on how to drive AI systems to be more creative and generate new ideas. This research invites us to reconsider traditional design methods and explore new approaches in an AI-driven world. Can we apply the same techniques used in traditional design, like the double diamond model, or do we need new methods for design engineering? How can we quickly design visualisations and craft new ideas with generative AI? This paper seeks to start this critical conversation and offers practical insights into the potential of AI in driving creativity in data visualisation.

Design Contradictions: Help or Hindrance?

TL;DR

This paper seeks to start a conversation on how to drive AI systems to be more creative and generate new ideas, and offers practical insights into the potential of AI in driving creativity in data visualisation.

Abstract

The need for innovative ideas in data visualisation drives us to explore new creative approaches. Combining two or more creative words, particularly those that contradict each other, can positively impact the creative process, sparking novel ideas and designs. As we move towards AI-driven design, an open question arises: do these design contradictions work positively with AI tools? Currently, the answer is no. AI systems, like large language models (LLMs), rely on algorithms that engender similarity, whereas creativity often requires divergence and novelty. This poster initiates a conversation on how to drive AI systems to be more creative and generate new ideas. This research invites us to reconsider traditional design methods and explore new approaches in an AI-driven world. Can we apply the same techniques used in traditional design, like the double diamond model, or do we need new methods for design engineering? How can we quickly design visualisations and craft new ideas with generative AI? This paper seeks to start this critical conversation and offers practical insights into the potential of AI in driving creativity in data visualisation.
Paper Structure (4 sections, 2 figures)

This paper contains 4 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work and Concepts
  3. Case Study - Round Bar Chart
  4. Conclusion

Figures (2)

  • Figure 1: This image demonstrates the design contradiction of round bar charts generated by ChatGPT for visualizing sales data. Each chart explores different styles, highlighting the challenge of maintaining clarity and comparability in a circular format.
  • Figure 2: This diagram maps the dimensions of design contradictions in visualisation. The vertical axis (“The mapping is…”) ranges from clear to farcical, while the horizontal axis (“The idea is…”) spans from common to contradictory. It helps analyse the balance between innovative ideas and practical execution in visualisations.