This study aims to propose applying the governance principles associated with the implementation of IFRS 9 to the algorithmic audit of artificial intelligence (AI) models used for credit rating. It examines how the principles of validation, documentation, traceability and forward-looking risk assessment used in the implementation of IFRS 9 can be adapted to the audit of AI systems.
The framework is validated on the Kaggle Credit Risk data set using four machine learning algorithms. It combines the model governance principles associated with the implementation of IFRS 9 with criteria for evaluating responsible AI. The assessment framework integrates predictive performance metrics alongside fairness evaluation measures, such as Disparate Impact and Demographic Parity. In addition, model interpretability is examined to identify the influence of sensitive attributes.
The results indicate that XGBoost achieves the highest predictive performance. However, all models exhibit varying degrees of dependence on sensitive features, particularly age and property ownership status, raising concerns regarding algorithmic bias in credit decision-making.
The proposed framework can help financial institutions improve the transparency and auditability of AI-based credit scoring systems. It provides auditors and regulators with a structured approach to assessing the performance, fairness and explainability of AI models in accordance with model governance principles.
This research establishes a link between the governance principles of models associated with the implementation of IFRS 9 and the ethical auditing of AI, two fields that are rarely associated with one another. It contributes to the development of a multidimensional framework that integrates predictive performance, algorithmic fairness and explainability for the auditing of AI-based credit scoring models.
Riham Ben Amor, Azhaar Lajmi· International Journal of Eth...· 0 citations
This study aims to examine the determinants of environmental, social and governance (ESG) practices within Tunisian companies, in order to identify how the specific characteristics of companies influence their ESG engagement in the context of an emerging market.
The analysis draws on survey data from 55 Tunisian firms across industrial, financial and consulting sectors. A principal component analysis (PCA)-based composite ESG index, aligned with the Tunis Stock Exchange framework, is constructed to assess performance across environmental, social and governance dimensions.
The results reveal that governance and social practices are relatively advanced, while environmental initiatives remain limited. Larger, older, listed and group-affiliated firms exhibit higher ESG performance, suggesting that organizational resources and institutional pressures play a critical role in shaping ESG engagement in Tunisia.
These findings provide insights for policymakers and corporate leaders. Strengthening regulatory incentives, improving ESG disclosure frameworks and supporting capacity-building initiatives could foster stronger environmental integration and more balanced ESG development across sectors. Furthermore, integrating ESG criteria into governance mechanisms and internal management tools appears essential to strengthen the alignment between financial performance and sustainable performance.
This paper contributes to the ESG literature by providing novel empirical evidence from Tunisia, an underexplored context in sustainability research. By constructing a PCA-based composite ESG index aligned with the Tunis Stock Exchange framework and analysing firm-level determinants of ESG engagement, it offers new insights into how institutional and organizational factors shape ESG adoption in emerging economies. Finally, the study combines methodological rigor with strong contextual grounding, providing a fresh perspective on ESG dynamics in environments characterized by evolving and heterogeneous institutional pressures.
Sina Belkhiria, Imen Bouchmel, Azhaar Lajmi et al.· International Journal of Dev...· 0 citations
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