I-Corps: Artificial Intelligence driven environmental, social, and governance

Trustees Of Boston University, Boston

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a technology that can help increase transparency and accountability in corporations' environmental, social, and governance practices. This, in turn, can encourage corporations to adopt more sustainable and responsible business practices, leading to a more sustainable future for both the environment and society. Moreover, by providing portfolio managers with comprehensive and accurate environmental, social, and governance metrics, the technology can help drive investment toward companies committed to sustainable and responsible practices. This shift in investment patterns can further encourage corporations to prioritize environmental, social, and governance considerations, leading to a virtuous cycle of increased environmental, social, and governance accountability and sustainability. The technology primarily aims to aid portfolio managers in assessing corporations' environmental, social, and governance responsibilities. In addition to investment advisers, the technology can help corporations improve their environmental, social, and governance reporting. Moreover, financial consultants can benefit from the technology as they advise and lead their clients towards better environmental, social, and governance-reporting practices increasingly demanded by regulators. The technology can increase environmental and social responsibilities within vulnerable communities and improve global governance. This I-Corps project is based on the development of technology to address issues with existing environmental, social, and governance standards and includes the environmental, social, and governance Knowledge Meta Model that consists of environmental, social, and governance Taxonomy and Ontology. The model analyzes environmental, social, and governance-related documents, including Securities and Exchange Commission Reports, environmental, social, and governance news, and social media posts from Twitter and Reddit, and creates a labeled environmental, social, and governance-related dataset. It further employs an environmental, social, and governance name, a Relation extraction model, multilabel classification for the taxonomy elements, and sentiment detection for environmental, social, and governance texts. These models are used to create the environmental, social, and governance Knowledge Graph Construction pipeline, which transforms environmental, social, and governance-related text into knowledge graphs. The constructed pipeline is used in real-time to convert texts from company reports and media documents into knowledge graphs. The methodology identifies discrepancies between the two sources using a graph comparison algorithm and reports any differences and inconsistencies between what the company reports and what the media writes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.