A Multidimensional Machine Learning Framework for Identifying Intersectional Pay Disparities and Ensuring ESG Regulatory Compliance

Shweta Anil Waghole

Abstract:As global Environmental, Social, and Governance (ESG) mandates become stricter in 2026, firms are under pressure to provide verifiable and transparent evidence of equitable compensation. Historically, pay audits relied on manual reviews of digital records, which were often slow, biased, and prone to oversight. This study introduces a sophisticated AI-driven methodology for "Organizational Equity Modeling". By utilizing Gradient Boosted Regression and 3D data visualization, the framework identifies hidden salary variances that standard linear reports often miss. Using a high fidelity dataset of 10,000 records, the models successfully detected intersectional gaps 4.2% higher than traditional methods. This system allows stakeholders to interact with a "digital twin" of their company’s pay structure to ensure fairpractices.