Artificial Intelligence and Corporate Social Responsibility: A Systematic Review of Emerging Integration, Mechanisms, and Challenges
人工知能と企業の社会的責任（CSR）：新たな統合、メカニズム、課題に関する体系的なレビュー (AI 翻訳)
Woon Leong Lin, A. Ignasiak-Szulc
🤖 gxceed AI 要約
日本語
この研究は、2013～2024年の文献を対象に、企業の社会的責任（CSR）への人工知能（AI）統合を体系的にレビュー。5つの実証クラスターと3つの生成メカニズム（データ化・監査、責任のアルゴリズム仲介、ステークホルダー顕著性の再重み付け）を特定し、AIがCSRパフォーマンス向上に寄与する一方で、説明責任の拡散や指標のクラウディングアウトなどのリスクも指摘。
English
This systematic review (2013–2024) examines AI integration into CSR, identifying five empirical clusters and three generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighting. It shows AI can improve CSR performance but also creates risks like accountability diffusion and metric crowding-out. The paper offers theoretical synthesis and propositions for future research.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJや統合報告書の文脈でCSR報告が重要視されており、AIを活用した透明性向上やステークホルダーエンゲージメント強化は、開示の質向上に寄与する可能性がある。ただし、本稿は汎用的なCSRを対象としており、気候関連開示に特化していない点に留意。
In the global GX context
Globally, AI in CSR governance is gaining traction with TCFD/ISSB frameworks emphasizing transparency. This review provides a mechanism-based understanding of how AI can enhance CSR responsiveness while cautioning against risks like assurance gaps and stakeholder exclusion, relevant for disclosure practitioners and regulators.
👥 読者別の含意
🔬研究者:The identified mechanisms (datafication, algorithmic mediation, stakeholder salience) offer a theoretical foundation for empirical studies on AI in CSR.
🏢実務担当者:Use the insights to design AI-driven CSR tools that balance efficiency with accountability, avoiding metric crowding-out.
🏛政策担当者:Note the risk of accountability diffusion and consider guidelines for transparent AI in CSR reporting.
📄 Abstract（原文）
This study examines the emerging integration of artificial intelligence (AI) into corporate social responsibility (CSR) through a systematic literature review covering 2013–2024. Drawing on PRISMA‐guided screening, thematic coding, and bibliometric mapping, the review identifies five recurrent empirical clusters: sustainability and resource optimization, ethical governance, transparency and accountability, stakeholder engagement, and CSR innovation. Rather than treating these clusters as the primary contribution, the study develops a mechanism‐based explanation of AI–CSR relationships. Specifically, the review highlights three recurring generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighting. These mechanisms help explain how AI can improve CSR performance, legitimacy, and responsiveness under specific boundary conditions, while also generating risks such as assurance gaps, accountability diffusion, stakeholder exclusion, and metric crowding‐out. The bibliometric evidence suggests increasing topical proximity and methodological coupling between AI and CSR but only limited evidence of full theoretical integration. Accordingly, the paper reframes the field as an emerging area of integration rather than a completed convergence. The study concludes by offering a more cautious theoretical synthesis, a set of aligned propositions, and an agenda for future research on the responsible use of AI in CSR governance and practice.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/isaf.70043first seen 2026-07-23 06:12:28
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