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Using Generative Art to Convey Past and Future Climate Transitions
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Harnessing human and machine intelligence for planetary-level climate action.

Ramit Debnath1,2, Felix Creutzig3,4, Benjamin K Sovacool5,6,7

  • 1Cambridge Zero and Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD United Kingdom.

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This summary is machine-generated.

Developing less biased artificial intelligence (AI) is crucial for climate action. This research proposes data-centric methods to create trustworthy AI systems that support planetary health and reduce data injustices.

Keywords:
Climate-change adaptationEngineering

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Area of Science:

  • Environmental Science
  • Computer Science
  • Sociology

Background:

  • Artificial intelligence (AI) development impacts society and the environment, influencing job markets and climate action.
  • Biased AI training datasets can destabilize political agencies and compromise social stability, potentially leading to societal tipping events.
  • Minimizing bias in AI is essential for effective climate change mitigation and adaptation strategies.

Purpose of the Study:

  • To address the critical need for less biased AI systems in climate action.
  • To explore data-centric knowledge generation for trustworthy AI in planetary health.
  • To investigate co-alignment of AI with an epistemic web for improved decision-making.

Main Methods:

  • Focus on data-centric knowledge generation to minimize AI bias.
  • Proposing a human-in-the-loop AI design.
  • Co-aligning AI with an epistemic web for planetary health challenges.

Main Results:

  • AI systems can be designed to minimize bias through data-centric approaches.
  • A human-in-the-loop AI can contribute to a planetary epistemic web supporting climate action.
  • AI can enable mitigation and adaptation interventions by understanding social tipping elements and reducing data injustices.

Conclusions:

  • A less biased, human-in-the-loop AI is paramount for trustworthy climate action decision-making.
  • Co-aligning AI with an epistemic web on planetary health is vital.
  • This approach can mitigate data injustices and support societal and environmental well-being.