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All Journal International Journal of Smart Systems International Journal of Technology and Modeling
Gupta, Aarav Sharma
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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A Comparative Study of Explainable AI Models in High-Stakes Decision-Making Systems Gupta, Aarav Sharma; Desai, Meera
International Journal of Smart Systems Vol. 1 No. 2 (2023): May
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i2.72

Abstract

High-stakes decision-making systems such as those used in healthcare, finance, and criminal justicedemand not only high predictive accuracy but also transparency to ensure trust, accountability, and ethical compliance. Explainable Artificial Intelligence (XAI) has emerged as a pivotal approach to address the black-box nature of complex machine learning models, offering interpretable insights into model predictions. This study presents a comparative analysis of leading XAI techniques, including SHAP, LIME, Counterfactual Explanations, and Rule-based Surrogates, across three real-world high-stakes domains. Using standardized evaluation metrics—fidelity, stability, usability, and computational efficiency—we examine the trade-offs between explanation quality and system performance. The results reveal that while SHAP consistently provides the highest fidelity explanations, it suffers from higher computational costs, whereas LIME offers faster, though sometimes less stable, explanations. Counterfactual methods excel in user interpretability but face challenges in generating plausible scenarios for complex datasets. Our findings highlight that no single XAI method is universally optimal; rather, the selection should align with domain-specific requirements and the criticality of the decisions involved. This comparative study contributes to the discourse on responsible AI deployment by providing actionable insights for practitioners, policymakers, and researchers seeking to integrate XAI into high-stakes environments.
Modelling the Impact of Climate Change on Agricultural Productivity: Case Studies from Developing Nations Gupta, Aarav Sharma; Kumar, Rahul; Desai, Meera; Shah, Rohan; Mehta, Neha
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.122

Abstract

Climate change poses a significant threat to agricultural productivity, particularly in developing nations where agriculture remains a primary livelihood source. This study presents a comprehensive modelling approach to assess the impact of climate variability on agricultural output, with a focus on case studies from India. Using a combination of climate projection data, crop simulation models, and econometric analyses, the research evaluates changes in temperature, precipitation patterns, and extreme weather events, and their implications for key staple crops such as rice and wheat. The study highlights regional disparities in vulnerability, adaptive capacity, and yield outcomes across different agro-climatic zones in India. Results indicate that without effective adaptation strategies, agricultural productivity could decline significantly in the coming decades, exacerbating food insecurity and rural poverty. The findings underscore the urgency of integrating climate resilience into national agricultural policies and promoting climate-smart agricultural practices. This research contributes to a broader understanding of how climate change affects agriculture in developing contexts and offers a methodological framework applicable to other regions facing similar challenges.
Co-Authors Desai, Meera Kumar, Rahul Mehta, Neha Shah, Rohan