Unmasking ESG Exaggerations Using Generative Artificial Intelligence

Yunfang Luo; Tao Yang; Qingan Li; Qiang Liu; Xiling Cui

Unmasking ESG Exaggerations Using Generative Artificial Intelligence

Yunfang Luo^*
, Tao Yang
, Qingan Li
, Qiang Liu^*
, Xiling Cui

^*Corresponding author for this work

Department of Accountancy, Economics and Finance

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

Exaggeration is a major indicator of greenwashing, typified by excessively optimistic or idealistic portrayals for environmental protection. The purpose of this study is to identify exaggerated information in environmental, social and governance (ESG) reports by using generative artificial intelligence (GenAI). We analyze a collection of ESG reports using three prompt engineering strategies: few-shot, zero-shot, and chain of thought (COT). We also cross-validate our results using traditional text analytics and human intelligence. Using this strategy, we evaluate exaggeration in ESG reports in a novel way using GenAI. The use of GenAI creates a strong foundation for further study in these and related fields.

Original language	English
Title of host publication	Proceedings of the 24th International Conference on Electronic Business, ICEB 2024
Editors	Eldon Y. Li, Patrick Y. K. Chau, Christy M. K. Cheung
Publisher	International Consortium for Electronic Business
Pages	684-689
Number of pages	6
Volume	24
Publication status	Published - Oct 2024
Event	24th International Conference on Electronic Business, ICEB 2024 - Zhuhai, China Duration: 24 Oct 2024 → 28 Oct 2024 https://iceb2024.johogo.com/ (Conference website) https://aisel.aisnet.org/iceb2024/ (Conference proceedings)

Publication series

Name	Proceedings of the International Conference on Electronic Business (ICEB)
Publisher	International Consortium for Electronic Business
ISSN (Print)	1683-0040

Conference

Conference	24th International Conference on Electronic Business, ICEB 2024
Country/Territory	China
City	Zhuhai
Period	24/10/24 → 28/10/24
Internet address	https://iceb2024.johogo.com/ (Conference website) https://aisel.aisnet.org/iceb2024/ (Conference proceedings)

User-Defined Keywords

GenAI
ESG
exaggerated information

Access to Document

https://aisel.aisnet.org/iceb2024/7

Cite this

Luo, Y., Yang, T., Li, Q., Liu, Q., & Cui, X. (2024). Unmasking ESG Exaggerations Using Generative Artificial Intelligence. In E. Y. Li, P. Y. K. Chau, & C. M. K. Cheung (Eds.), Proceedings of the 24th International Conference on Electronic Business, ICEB 2024 (Vol. 24, pp. 684-689). (Proceedings of the International Conference on Electronic Business (ICEB)). International Consortium for Electronic Business. https://aisel.aisnet.org/iceb2024/7

Luo, Yunfang ; Yang, Tao ; Li, Qingan et al. / Unmasking ESG Exaggerations Using Generative Artificial Intelligence. Proceedings of the 24th International Conference on Electronic Business, ICEB 2024. editor / Eldon Y. Li ; Patrick Y. K. Chau ; Christy M. K. Cheung. Vol. 24 International Consortium for Electronic Business, 2024. pp. 684-689 (Proceedings of the International Conference on Electronic Business (ICEB)).

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title = "Unmasking ESG Exaggerations Using Generative Artificial Intelligence",

abstract = "Exaggeration is a major indicator of greenwashing, typified by excessively optimistic or idealistic portrayals for environmental protection. The purpose of this study is to identify exaggerated information in environmental, social and governance (ESG) reports by using generative artificial intelligence (GenAI). We analyze a collection of ESG reports using three prompt engineering strategies: few-shot, zero-shot, and chain of thought (COT). We also cross-validate our results using traditional text analytics and human intelligence. Using this strategy, we evaluate exaggeration in ESG reports in a novel way using GenAI. The use of GenAI creates a strong foundation for further study in these and related fields.",

keywords = "GenAI, ESG, exaggerated information",

author = "Yunfang Luo and Tao Yang and Qingan Li and Qiang Liu and Xiling Cui",

note = "This work is partially supported by the Beijing Municipal Education Commission (Grant No. 22019821001) in China. Publisher Copyright: {\textcopyright} 2024 International Consortium for Electronic Business. All rights reserved.; 24th International Conference on Electronic Business, ICEB 2024 ; Conference date: 24-10-2024 Through 28-10-2024",

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editor = "Li, \{Eldon Y.\} and Chau, \{Patrick Y. K.\} and Cheung, \{Christy M. K.\}",

booktitle = "Proceedings of the 24th International Conference on Electronic Business, ICEB 2024",

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Luo, Y, Yang, T, Li, Q, Liu, Q & Cui, X 2024, Unmasking ESG Exaggerations Using Generative Artificial Intelligence. in EY Li, PYK Chau & CMK Cheung (eds), Proceedings of the 24th International Conference on Electronic Business, ICEB 2024. vol. 24, Proceedings of the International Conference on Electronic Business (ICEB), International Consortium for Electronic Business, pp. 684-689, 24th International Conference on Electronic Business, ICEB 2024, Zhuhai, China, 24/10/24. <https://aisel.aisnet.org/iceb2024/7>

Unmasking ESG Exaggerations Using Generative Artificial Intelligence. / Luo, Yunfang; Yang, Tao; Li, Qingan et al.
Proceedings of the 24th International Conference on Electronic Business, ICEB 2024. ed. / Eldon Y. Li; Patrick Y. K. Chau; Christy M. K. Cheung. Vol. 24 International Consortium for Electronic Business, 2024. p. 684-689 (Proceedings of the International Conference on Electronic Business (ICEB)).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Unmasking ESG Exaggerations Using Generative Artificial Intelligence

AU - Luo, Yunfang

AU - Yang, Tao

AU - Li, Qingan

AU - Liu, Qiang

AU - Cui, Xiling

N1 - This work is partially supported by the Beijing Municipal Education Commission (Grant No. 22019821001) in China. Publisher Copyright: © 2024 International Consortium for Electronic Business. All rights reserved.

PY - 2024/10

Y1 - 2024/10

N2 - Exaggeration is a major indicator of greenwashing, typified by excessively optimistic or idealistic portrayals for environmental protection. The purpose of this study is to identify exaggerated information in environmental, social and governance (ESG) reports by using generative artificial intelligence (GenAI). We analyze a collection of ESG reports using three prompt engineering strategies: few-shot, zero-shot, and chain of thought (COT). We also cross-validate our results using traditional text analytics and human intelligence. Using this strategy, we evaluate exaggeration in ESG reports in a novel way using GenAI. The use of GenAI creates a strong foundation for further study in these and related fields.

AB - Exaggeration is a major indicator of greenwashing, typified by excessively optimistic or idealistic portrayals for environmental protection. The purpose of this study is to identify exaggerated information in environmental, social and governance (ESG) reports by using generative artificial intelligence (GenAI). We analyze a collection of ESG reports using three prompt engineering strategies: few-shot, zero-shot, and chain of thought (COT). We also cross-validate our results using traditional text analytics and human intelligence. Using this strategy, we evaluate exaggeration in ESG reports in a novel way using GenAI. The use of GenAI creates a strong foundation for further study in these and related fields.

KW - GenAI

KW - ESG

KW - exaggerated information

UR - https://www.scopus.com/pages/publications/85213406350

M3 - Conference proceeding

AN - SCOPUS:85213406350

VL - 24

T3 - Proceedings of the International Conference on Electronic Business (ICEB)

SP - 684

EP - 689

BT - Proceedings of the 24th International Conference on Electronic Business, ICEB 2024

A2 - Li, Eldon Y.

A2 - Chau, Patrick Y. K.

A2 - Cheung, Christy M. K.

PB - International Consortium for Electronic Business

T2 - 24th International Conference on Electronic Business, ICEB 2024

Y2 - 24 October 2024 through 28 October 2024

ER -

Luo Y, Yang T, Li Q, Liu Q, Cui X. Unmasking ESG Exaggerations Using Generative Artificial Intelligence. In Li EY, Chau PYK, Cheung CMK, editors, Proceedings of the 24th International Conference on Electronic Business, ICEB 2024. Vol. 24. International Consortium for Electronic Business. 2024. p. 684-689. (Proceedings of the International Conference on Electronic Business (ICEB)).

Unmasking ESG Exaggerations Using Generative Artificial Intelligence

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