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Related Experiment Video

Updated: May 1, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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A reflexive artificial intelligence governance for transformative change in sustainability.

Anna Hausmann1,2,3, Tuuli Toivonen4,5, Gonzalo Cortés-Capano6,7

  • 1Department of Biological and Environmental Science, School of Resource Wisdom, University of Jyväskylä, Survontie 9c, 40500, Jyväskylä, Finland. anna.hausmann87@gmail.com.

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Summary

This study introduces reflexive artificial intelligence (AI) governance to address sustainability challenges. It moves beyond technical fixes, promoting democratic values and inclusive decision-making for transformative change.

Keywords:
Environmental governanceEnvironmental justiceEthicsReflexivityTransformative change

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Area of Science:

  • Environmental Science
  • Computer Science
  • Political Science

Background:

  • Artificial intelligence (AI) is increasingly used in sustainability governance.
  • Current AI applications focus on optimization and prediction, oversimplifying complex social-ecological issues.
  • This technical focus overlooks crucial aspects like plural values, lived experiences, and democratic input.

Purpose of the Study:

  • To advance a reflexive AI governance approach.
  • To treat AI as a socio-technical system influencing problem framing, knowledge, and authority.
  • To align AI with plural, justice-oriented transformation pathways in sustainability.

Main Methods:

  • Synthesizing material, technical, epistemic, and ethico-political challenges.
  • Drawing on Aristotelian concepts of techne, episteme, and phronesis.
  • Outlining three dimensions of reflexivity: design, epistemological, and engagement.

Main Results:

  • Demonstrating how reflexivity can align AI with sustainability goals.
  • Illustrating the approach using a four-phase governance cycle and a protected area management scenario.
  • Showing how AI can foster deliberation, inclusivity, and ecological sufficiency.

Conclusions:

  • Reflexive AI governance supports democratic capacities for AI use in sustainability.
  • It enables informed decisions on AI implementation, including non-use, restriction, or withdrawal.
  • This approach grounds AI in sustainability visions for transformative change.