Machine Learning in Corporate Financial Sustainability: A Critical Evaluation of Models Bias and Outcomes
コーポレート・ファイナンシャル・サステナビリティにおける機械学習：モデルのバイアスと結果の批判的評価 (AI 翻訳)
Z. Baharom
🤖 gxceed AI 要約
日本語
本論文は、企業の財務的サステナビリティ（CFS）における機械学習（ML）の活用を批判的にレビューし、ESGスコアリングや予測分析でのML導入が「自動化されたグリーンウォッシング」や構造的不平等を強化するリスクを指摘する。透明性・説明可能性、バイアス監査、ESG-ML統合報告基準、ステークホルダー参加を含む４つの柱からなる責任あるML導入フレームワークを提案し、効果的なガバナンスの重要性を強調している。
English
This critical review examines machine learning (ML) in corporate financial sustainability (CFS), highlighting risks of automated greenwashing and reinforced inequalities in ESG scoring and predictive analytics. It proposes a four-pillar framework—transparent models, bias audits, integrated ESG-ML reporting, and stakeholder inclusion—to ensure responsible ML deployment, emphasizing governance over techno-optimism.
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開示基準や有報でのサステナビリティ情報記載が進む中、MLを活用したESG評価や報告の自動化が拡大しつつある。本論文は、こうした技術導入に伴う「自動化されたグリーンウォッシング」リスクや説明責任の低下を警告しており、日本企業のAIガバナンスと開示品質向上に示唆を与える。
In the global GX context
Globally, with TCFD, ISSB, and CSRD pushing for robust sustainability disclosures, AI-driven ESG tools are proliferating. This paper critically evaluates ML's risks—bias, opacity, greenwashing—and offers a governance framework, which is timely for regulators and standard-setters seeking to ensure technology supports, not undermines, genuine transparency.
👥 読者別の含意
🔬研究者:Highlights underexplored ML risks in ESG and provides a framework for empirical testing of bias and governance mechanisms.
🏢実務担当者:Offers a checklist (four pillars) for corporate teams deploying ML in sustainability reporting to avoid greenwashing and ensure accountability.
🏛政策担当者:Supports the case for regulation of AI in financial sustainability, including audit standards and transparency requirements.
📄 Abstract（原文）
The integration of machine learning (ML) into corporate financial sustainability (CFS) is a double-edged sword: while offering transformative potential for predictive analytics, risk modeling, and reporting efficiency, it also introduces significant risks of algorithmic bias, opacity, and erosion of accountability. This critical literature review synthesizes 31 peer-reviewed articles to evaluate ML's role in CFS contexts, moving beyond techno-optimism to foreground ethical and governance challenges. Our analysis reveals that current applications, such as ESG scoring, predictive CFS analytics, and automated reporting, often prioritize scalability and efficiency over validity, equity, and substantive performance, thereby risking “automated greenwashing” and reinforcing structural inequalities. In response, we propose an integrative, four-pillar framework for responsible ML deployment, emphasizing Transparent and Explainable Models, Bias-Audit and Ethical Governance, Integrated ESG-ML Reporting Standards, and Stakeholder-Inclusive ML Deployment. Institutional, technological, and cultural contexts moderate the effectiveness of these pillars. We argue that without deliberate governance, ML may undermine the very goals of sustainable value creation it seeks to advance. This review calls for interdisciplinary collaboration, standardized auditing protocols, and proactive regulation to align ML innovation with the long-term imperatives of CFS.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.54536/ajarai.v1i1.6814first seen 2026-05-15 18:46:55
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。