MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering

Chekalina, V.; Razzhigaev, Anton; Sayapin, Albert; Panchenko, Alexander

doi:10.48550/arxiv.2204.10629

Cited by 1 publication

(1 citation statement)

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“…In the case of investors, the most practical application of AI is concerned with the automatic assessment of company performance based on its public press releases, financial reports, social review summaries, and news articles [63]. This approach implements natural language processing (NLP) algorithms, which can rapidly scan through the text to extract specific words (such as places, dates, and names) and generate summary reports [64]. In particular, in [63], the authors discussed a special model, esgNLP, which was initially created by using a pretrained Google BERT general English language model [65] and then further trained on ESG reports.…”

Section: Governancementioning

confidence: 99%

Practical AI Cases for Solving ESG Challenges

Burnaev,

Mironov,

Shpilman

et al. 2023

Sustainability

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Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG. The current paper fills this gap by reviewing practical AI applications with a main focus on IT and engineering implementations. The considered cases are based on almost one hundred publicly available research manuscripts and reports obtained via online search engines. This review involves the study of typical business and production problems associated with each ESG domain, gives background details on several selected cases (such as carbon neutrality, land management, and ESG scoring), and lists challenges that the smart algorithms can pose (such as fake news generation and increased electricity consumption). Overall, it is concluded that, while many practical cases already exist, AI in ESG is still very far away from reaching its full potential; however, one should always remember that AI itself can lead to some ESG risks.

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Section: Governancementioning

confidence: 99%