Articlehybrid2026

From promise to concern: Public perceptions of AI in ESG frameworks over time

11 citations0/0 files storedTechnology in Society

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Ethics and Social Impacts of AI Corporate Social Responsibility Reporting Innovation, Sustainability, Human-Machine Systems

Abstract

This study investigates how public sentiment toward the integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) frameworks has evolved over the past 25 years. Drawing on a dataset of 22,454 news articles published between 2000 and 2025, we conduct a large-scale longitudinal sentiment analysis to identify discursive patterns in the perception of AI's role across the ESG dimensions. Our findings reveal substantial variation across the three pillars. While sentiment toward AI in governance contexts shows a consistently positive trend—associated with increased expectations for transparency, monitoring, and compliance—environmental sentiment exhibits a sharp downturn after 2022, reflecting concerns over the carbon footprint of generative AI technologies. The social dimension displays fluctuating sentiment, influenced by debates on automation, fairness, and ethical accountability. These differentiated trajectories suggest that AI legitimacy is a domain-specific and socially negotiated construct, rather than a uniform outcome of technological advancement. Public discourse, as captured in news media, functions as an anticipatory indicator of emerging regulatory tensions and reputational risks, offering valuable foresight for corporate and institutional decision-makers. This study contributes to the literature on technology and society by highlighting the role of sentiment dynamics in shaping AI governance and sustainability strategies. It provides both theoretical insights into the social construction of technological legitimacy and practical implications for the design of responsive, context-sensitive ESG policies in the age of digital transformation.

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References

2019Content Analysis: An Introduction to Its Methodology 2018AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations 2015Why Are There Still So Many Jobs? The History and Future of Workplace Automation 2005An Examination of Differences Between Organizational Legitimacy and Organizational Reputation*2008Text Mining Infrastructure in R 2020Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda 2016tidytext: Text Mining and Analysis Using Tidy Data Principles in R 2019Corporate digital responsibility

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