Explainable artificial intelligence (XAI) for trustworthy decision-making
DOI:
https://doi.org/10.35335/cit.Vol15.2023.622.pp240-246Keywords:
Ethical Decision Making, Fairness and Accountability, Loan Approval Optimization, Transparency in AI, Trustworthy AIAbstract
This research delves into the optimization of loan approval decisions by integrating the Trustworthy Decision Making (TDM) framework into a mathematical model. The study aims to strike a balance between maximizing loan approvals and ensuring fairness, transparency, and accountability in AI-driven decision-making processes. Leveraging principles of transparency, fairness, and accountability, the mathematical model seeks to optimize loan approvals while adhering to ethical considerations. The formulation emphasizes the importance of interpretable models to maintain transparency in decision explanations, ensuring alignment with trustworthy AI practices. Implementation results demonstrate the efficacy of the model in achieving a balanced approval rate across demographic groups while providing transparent explanations for decisions. This study highlights the significance of ethical considerations and mathematical formulations in fostering responsible AI implementations. However, continual refinement and adaptation of such models remain essential to align with evolving ethical standards and societal expectations. Overall, this research contributes to the discourse on responsible AI by showcasing a methodological approach that integrates ethical principles and mathematical formulations to promote fairness, transparency, and accountability in AI-driven decision-making.
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Copyright (c) 2023 Deni Kurniawan, Lise Pujiastuti
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