Year: 2025
Type: article
Abstract: : Traditional methods of measuring Corporate Social Responsibility (CSR) are frequently plagued by serious shortcomings, such as over-dependence on company-reported data, absence of real-time responsiveness, and narrow representation of public opinion or third-party views. These shortcomings lead to CSR measurements that are prone to bias, static, and divorced from the social context. As business responsibility and ethical investing come to the forefront increasingly, there is a pressing need for stronger, data-based solutions that can capture an organization's actual social footprint. This paper presents an AI-based system for CSR impact measurement that closes these gaps by tapping into publicly available data from diverse, real-time sources like news media, social media, and government and NGO databases. Leveraging natural language processing capability and machine learning, the system extracts unstructured text data and condenses it into quantifiable CSR scores. Scores are metrics of organizational contributions to sustainability, community, and ethics. The system is designed to provide transparent, dynamic, and comparative assessment of corporate behavior to enable stakeholders to make informed decisions based on comparative evidence-based evaluation. This effort further contributes to the advancement of automatic, objective CSR measurement techniques in terms of society context and real conversation.
Author: Bhoomika S Kolekar
Institution: Global Academy of Technology
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4410157639