Ethical AI-Driven Cloud Software Engineering Framework for Financial Inclusion: Integrating Safe Reinforcement Learning in Web Application Development
DOI:
https://doi.org/10.15662/IJRAI.2021.0406010Keywords:
Ethical AI, Cloud Software Engineering, Financial Inclusion, Safe Reinforcement Learning, Responsible AI, Web Application Development, Federated Learning; Explainable AI, Fairness, Digital Trust, Human-in-the-Loop, AI GovernanceAbstract
The rapid adoption of artificial intelligence (AI) in cloud-based financial systems has transformed access to digital financial services, particularly in underserved communities. However, the integration of AI—especially reinforcement learning (RL)—into web applications raises significant ethical, safety, and transparency challenges. This paper proposes an Ethical AI-Driven Cloud Software Engineering Framework that embeds Safe Reinforcement Learning (Safe-RL) methodologies into the full software development lifecycle for financial web applications. The framework leverages cloud-native architectures, federated data governance, and responsible machine learning (ML) pipelines to ensure fairness, explainability, and compliance with financial regulations. A multi-layered ethical design model is introduced, combining formal verification, human-in-the-loop decision control, and bias-aware policy optimization. Through simulated case studies in micro-lending and credit scoring systems, the framework demonstrates how Safe-RL agents can adapt to user behavior while preserving data privacy, algorithmic accountability, and equitable access. The results highlight the potential of ethically aligned, AI-driven cloud software engineering to accelerate financial inclusion and digital trust across emerging markets.
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