Toward Trustworthy ESG Risk Assessment through XAI: a State-of-the-Art Review

Hossein Habibinejad, Morteza Alaeddini () and Paul Reaidy ()
Additional contact information
Hossein Habibinejad: CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes
Morteza Alaeddini: ICN Business School, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Paul Reaidy: CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes

Post-Print from HAL

Abstract: As artificial intelligence (AI) becomes increasingly central to environmental, social, and governance (ESG) risk assessment, concerns about model opacity and stakeholder trust have come to the forefront Traditional ESG scoring systems face limitations such as inconsistent data, lack of transparency, and potential bias issues that are often exacerbated by complex, black-box AI models. This paper examines the role of explainable AI (XAI) and responsible AI (RAI) in enhancing the credibility and ethical alignment of ESG assessments. A comprehensive review of the literature highlights critical research gaps, including the absence of standardised explainability metrics, minimal empirical validation in real-world contexts, and the neglect of cultural variability in trust formation. To address these gaps, the paper introduces a theoretical framework that integrates trust determinants, RAI principles, and XAI techniques. The model also incorporates human-centric moderators and feedback loops to ensure adaptability across stakeholder groups. By linking interpretability, ethical safeguards, and user-centred design, the framework offers a path toward more trustworthy and transparent ESG systems. Ultimately, this study contributes to the development of AI-powered tools that support responsible decision-making in sustainable finance while reinforcing stakeholder confidence and accountability.

Keywords: Explainable Artificial Intelligence (XAI); Responsible Artificial Intelligence (RAI); ESG Risk Assessment; Sustainable Finance; Investment; Trustworthiness; Transparency; Fairness; Accountability. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Published in International Journal of Generative Artificial Intelligence in Business, 2025, 1 (1), ⟨10.1504/IJGAIB.2025.10073995⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05356025

DOI: 10.1504/IJGAIB.2025.10073995

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().