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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning International Journal of Sustainable Business, Management and Accounting
Amel Zulfukar Hassan Adlan
Nile Valley University
Computer Science & IT Economics, Econometrics & Finance Social Sciences

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The Role of Artificial Intelligence in Enhancing Sustainable Accounting and ESG Reporting: A Systematic Literature Review Eko Risdianto; Amel Zulfukar Hassan Adlan; Usmanova Shokhsanam Avazovna
International Journal of Sustainable Business, Management and Accounting Vol. 2 No. 1 (2026): International Journal of Sustainable Business, Management, and Accounting (IJSB
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsbma.v2i1.170

Abstract

Background of study: The increasing demand for sustainability and transparency has strengthened the role of sustainable accounting and Environmental, Social, and Governance (ESG) reporting in modern business practices. However, traditional reporting systems face significant challenges in managing complex, large-scale, and heterogeneous ESG data, leading to limitations in accuracy, consistency, and timeliness. Although artificial intelligence (AI) has been widely adopted in financial analysis, its application in sustainable accounting and ESG reporting remains fragmented and underexplored. Aims: This study aims to provide a comprehensive and structured analysis of the role of AI in enhancing sustainable accounting and ESG reporting. Methods: This study employs a Systematic Literature Review (SLR) using the PRISMA framework, combined with bibliometric analysis. Data were collected from the Scopus database using three keyword strategies and filtered based on predefined inclusion criteria. The dataset was cleaned using OpenRefine and analyzed using VOSviewer and Biblioshiny to explore research trends, thematic structures, and intellectual development. Result: The results show that AI technologies, particularly machine learning, natural language processing, and big data analytics, significantly improve ESG data processing, reporting efficiency, and predictive decision-making. However, ESG reporting practices remain fragmented, lack standardization, and are often implemented in isolated contexts. Conclusion: This study contributes by integrating AI, sustainable accounting, and ESG reporting into a unified perspective supported by systematic and bibliometric analysis. The findings highlight the potential of AI to enhance transparency and efficiency in sustainability reporting, while emphasizing the need for integrative frameworks and empirical validation in future research.
Failure Mode Analysis of Machine Learning Models in Realistic Data Deployment Scenarios Lau Meng Cheng; Amel Zulfukar Hassan Adlan
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.651

Abstract

Background: Machine learning models frequently demonstrate strong performance under controlled benchmark evaluations. However, such evaluations often fail to capture hidden vulnerabilities that emerge under realistic deployment conditions. In real-world environments, models are exposed to stressors such as label corruption, feature noise, distributional shifts, and operational constraints, including reduced computational precision and increased latency. These conditions can induce performance degradation and structural instability, highlighting the need for a systematic robustness evaluation framework that goes beyond conventional accuracy metrics.Aims: This paper aims to introduce a formalized Failure Mode Analysis Protocol (FMAP) for evaluating machine learning model robustness under realistic operational stressors. The study reconceptualizes robustness evaluation as a distribution-based process, where model deployment itself generates a new distribution over time.Methods: The proposed FMAP framework evaluates model behavior under progressively adverse conditions, including symmetric label corruption, additive feature noise, distributional shifts, and operational constraints such as reduced numerical precision and increased inference latency. Experiments were conducted across diverse tabular and image benchmark datasets using representative model architectures, including linear models, ensemble methods, margin-based models, and deep neural networks.Result: The experiments reveal distinct robustness profiles across model architectures when exposed to escalating stress conditions. Operational constraints and compositional limitations were shown to induce measurable degradation patterns, including instability and output collapse under extreme stress. The findings demonstrate that model failure is not solely a function of predictive accuracy loss but is closely linked to operational constraints and evolving distributional conditions. The distribution-based evaluation framework effectively captures early-stage degradation and full failure transitions.Conclusion: This study establishes a structured protocol for analyzing machine learning failure modes under realistic deployment scenarios. By framing robustness evaluation as a distribution-based process, the FMAP approach provides a systematic method for identifying operational risks and structural vulnerabilities.
Co-Authors Eko Risdianto Lau Meng Cheng Usmanova Shokhsanam Avazovna