Pratical AI cases for Solving ESG challenges
The article "Practical AI Cases for Solving ESG Challenges" by Burnaev et al. reviews the practical applications of artificial intelligence (AI) in addressing environmental, social, and governance (ESG) challenges, focusing primarily on IT and engineering implementations. The authors emphasize that while AI is widely discussed in relation to ESG, there is a lack of focus on concrete, practical cases that integrate all three domains. The paper aims to fill this gap by exploring how AI can solve specific ESG-related problems, such as carbon neutrality, land management, and ESG scoring. It also addresses the challenges that AI itself can pose, including the generation of fake news and increased electricity consumption.
Key Messages:
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Practical Applications of AI for ESG: The article highlights several real-world cases where AI tools have been used to tackle ESG issues. For example, AI is employed in environmental monitoring to optimize resource use and reduce carbon footprints, in social contexts to improve healthcare and education through predictive modeling, and in governance to enhance transparency and decision-making processes through natural language processing (NLP) and machine learning (ML) models.
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Challenges and Risks of AI in ESG: The authors point out that while AI presents numerous opportunities, it also brings risks. These include ethical concerns, such as the potential for AI to perpetuate biases and privacy violations, as well as technical and environmental challenges, such as high energy consumption and the generation of misleading or harmful content (e.g., fake news).
Original Ideas and Unique Contributions:
- Systematic Review of Practical AI Applications: One of the most original contributions of the article is its systematic review of nearly 100 publicly available research manuscripts and reports, providing a comprehensive overview of AI applications across all three ESG domains.
- Dual Perspective on AI: The paper uniquely emphasizes both the positive applications of AI in solving ESG problems and the potential negative impacts of AI technologies themselves, suggesting the need for responsible AI governance to balance these dynamics.
