Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sinha, Swati; Chopra, Puja Title A name given to the resource Algorithmic Governance in ESG Ratings: Addressing Bias and Enhancing Transparency in AI- Driven Sustainable Finance Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Behavioral Finance and AI Tools for Sustainability;pp.375-401 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-9684-1.ch015" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9684-1.ch015</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021761225?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021761225?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sinha S., Christ University, India; Chopra P., Christ University, India Description An account of the resource 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&amp;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. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836939686-5; 979-836939684-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online