USING ARTIFICIAL INTELLIGENCE TO EVALUATE ESG INDICATORS IN HR PRACTICES

The article examines the potential applications of artificial intelligence (AI) tools for assessing ESG indicators in HR practices. The relevance of this study is driven by the increasing role of environmental, social, and governance (ESG) factors in corporate management and the need for their effective analysis. Traditional ESG assessment methods, such as expert evaluations, internal audits, and reporting, are resource-intensive and do not always ensure objectivity. In contrast, AI enables the automation of data collection, analysis, and interpretation, facilitating timely strategic decision-making and minimizing human bias. The purpose of the study is to explore the potential of AI in ESG assessment within HR practices, identify its key advantages, and outline prospects for its integration into business processes. The scientific novelty of the research lies in a comprehensive approach to integrating AI into ESG assessment in HR, improving corporate culture monitoring, evaluating corporate social responsibility, overseeing HR policies, and ensuring adherence to sustainable development principles. The author’s contribution includes identifying key directions for AI application in HR analytics, substantiating its advantages in ESG assessment, and developing approaches for implementing such technologies in personnel management systems. The practical significance of the study’s findings lies in the potential implementation of AI tools to enhance business process transparency, improve HR policies, and optimize ESG data collection and analysis. AI-driven solutions contribute to the development of effective HR strategies, strengthen corporate reputation, and increase stakeholder trust. The main conclusions confirm that AI implementation in ESG assessment within HR practices is both relevant and necessary for modern companies. AI adoption enhances assessment accuracy, reduces the time required for ESG data processing, and improves the quality of managerial decision-making. Future research in this field may focus on refining AI-based analytical algorithms and adapting them to the specific needs of different business sectors.