Algorithmic Governance in ESG Ratings: Addressing Bias and Enhancing Transparency in AI- Driven Sustainable Finance
ESG ratings serve as guides for corporate investment and strategic decisions and are appended with numerous biases, inconsistencies, and lack of transparency. The present chapter attempts to examine the ratings of 100 companies from different industries and regions and compare S&P Global with MSCI and Sustainalytics. The chapter researches the effects of size, sector, and regional biases on scores using a mixed- method approach. Findings show that energy- intensive sectors are downgraded because of sustainability efforts; large firms get higher grades due to better disclosure, and performance is also higher for companies in jurisdictions marked by stringent regulations. Inconsistencies in ratings are illustrated further by examples of the specific study cases that deal with BlackRock and Tesla. This chapter brings an AI- enabled framework that uses blockchain and machine learning, as well as real- time information, to boost transparency and standardization in response to these issues. 2025 by IGI Global Scientific Publishing. All rights reserved.
Behavioral Finance and AI Tools for Sustainability;pp.375-401
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.4018/979-8-3693-9684-1.ch015 https://www.scopus.com/pages/publications/105021761225?origin=resultslist
Sinha S., Christ University, India; Chopra P., Christ University, India