TY - GEN
T1 - CAN GENERATIVE ARTIFICIAL INTELLIGENCE (AI) ASSISTANTS’ EVALUATION OF ENVIRONMENTAL, SOCIAL, AND GOVERNANCE (ESG) PERFORMANCE REPLACE PROFESSIONAL EVALUATION?
AU - Chan, Victor K.Y.
N1 - Publisher Copyright:
© 2024 Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024. All rights reserved.
PY - 2024
Y1 - 2024
N2 - This article explores how evaluation of companies’/stocks’ environmental, social, and governance (ESG) performance by generative artificial intelligence (AI) compares with traditional, proprietary, professional evaluation, and whether the former is able to replace the latter. The generative AI assistant utilized in the underlying study was Microsoft Copilot, which was requested to accord rating scores to the three individual ESG components, namely, (1) Environmental, (2) Social, and (3) Governance of the top 40 companies/stocks among the S&P 500. The traditional, proprietary, professional evaluation of the companies’/stocks’ ESG performance for these three components adopted in this article was the rating scores by Sustainalytics of Morningstar. The correlation coefficient between Copilots’ rating score for each of these three components over the top 40 companies/stocks and the corresponding rating score from Sustainalytics was computed. Subsequently, multiple regression of Sustainalytics’s ESG Risk Rating score (i.e., a summary ESG score from Sustainalytics as the dependent variable) on Copilot’s rating scores for the three components above (as the independent variables) over the top 40 companies/stocks was performed. It was found that the correlation coefficients were respectively -.576 (p = 0.000), -.166 (p = .306), and -.171 (p = .291). The multiple regression included Copilot’s rating scores for all the three components above as the independent variables with the R2 = .441, the F-test’s F statistic = 9.486 (df = (3, 36) and p = 0.000), the respective regression coefficients being -5.886, 2.176, and -2.185 and the corresponding t-tests’ t values being -3.289 (p = 0.002), .955 (p = .346, and -1.075 (p =.290).
AB - This article explores how evaluation of companies’/stocks’ environmental, social, and governance (ESG) performance by generative artificial intelligence (AI) compares with traditional, proprietary, professional evaluation, and whether the former is able to replace the latter. The generative AI assistant utilized in the underlying study was Microsoft Copilot, which was requested to accord rating scores to the three individual ESG components, namely, (1) Environmental, (2) Social, and (3) Governance of the top 40 companies/stocks among the S&P 500. The traditional, proprietary, professional evaluation of the companies’/stocks’ ESG performance for these three components adopted in this article was the rating scores by Sustainalytics of Morningstar. The correlation coefficient between Copilots’ rating score for each of these three components over the top 40 companies/stocks and the corresponding rating score from Sustainalytics was computed. Subsequently, multiple regression of Sustainalytics’s ESG Risk Rating score (i.e., a summary ESG score from Sustainalytics as the dependent variable) on Copilot’s rating scores for the three components above (as the independent variables) over the top 40 companies/stocks was performed. It was found that the correlation coefficients were respectively -.576 (p = 0.000), -.166 (p = .306), and -.171 (p = .291). The multiple regression included Copilot’s rating scores for all the three components above as the independent variables with the R2 = .441, the F-test’s F statistic = 9.486 (df = (3, 36) and p = 0.000), the respective regression coefficients being -5.886, 2.176, and -2.185 and the corresponding t-tests’ t values being -3.289 (p = 0.002), .955 (p = .346, and -1.075 (p =.290).
KW - AI
KW - Artificial Intelligence
KW - Environmental
KW - ESG
KW - Governance
KW - Social
UR - http://www.scopus.com/inward/record.url?scp=85214115835&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85214115835
T3 - Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024
SP - 301
EP - 308
BT - Proceedings of the International Conferences on Applied Computing and WWW/Internet 2024
A2 - Miranda, Paula
A2 - Isaias, Pedro
A2 - Isaias, Pedro
A2 - Rodrigues, Luis
PB - IADIS Press
T2 - 21st International Conference on Applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024
Y2 - 26 October 2024 through 28 October 2024
ER -