Human-AI Collaboration for Environmental Sustainability - Use Cases, Impacts, and Modes of Augmentation

EasyChair Preprint 15325

Abstract

The world is currently facing a range of environmental challenges, including climate change, resource depletion, and biodiversity loss. Human-AI collaboration (HAIC) offers new momentum for addressing these issues. However, there is no comprehensive understanding of how HAIC can impact environmental sustainability. We conducted a systematic literature review on HAIC for Environmental Sustainability (ES). After analyzing 35 relevant articles, we identified 19 HAIC use cases for ES, such as smart sustainable mobility, route planning, and species identification. The use cases show the potential of HAIC to positively impact ES. For instance, HAIC contributes to energy conservation, waste and pollution reduction, and biodiversity preservation. In addition, we identified three main modes of augmentation in HAIC for ES: decision support, interaction and adaptation, and engagement and communication. Based on our review findings, we outline a research agenda, highlighting gaps such as the lack of studies contemplating the organizational level of HAIC.

Keyphrases: Environmental sustainability (ES), Human-AI collaboration (HAIC), Information Systems (IS), Sustainability, Systematic Literature Review (SLR)

@booklet{EasyChair:15325,
  author    = {Yao Zhang and Alexander Richter and Yvonne Hong},
  title     = {Human-AI Collaboration for Environmental Sustainability - Use Cases, Impacts, and Modes of Augmentation},
  howpublished = {EasyChair Preprint 15325},
  year      = {EasyChair, 2024}}