<b>Ethical Dilemmas in AI: Generative Models in Finance and Healthcare</b>

This research article, "<b>Ethical Dilemmas in AI: Generative Models in Finance and Healthcare"</b> authored by <b>Srinivasan Venkataramanan, </b><b>Sai Manoj Yellepeddi, Ajay Aakula, Venkata Sri Manoj Bonam, Mohammed Ahmed, and Meccy Joy</b>, critically examines the ethical challenges posed by the deployment of generative AI models in the high-stakes domains of finance and healthcare. As technologies like GANs, VAEs, and GPT-based large language models become increasingly integrated into financial services and medical applications, they offer powerful capabilities in fraud detection, credit scoring, diagnostics, and drug discovery. However, these advancements also bring serious concerns regarding fairness, bias, data privacy, consent, accountability, and transparency.The study uses a mixed-methods approach to explore technical use cases and ethical dilemmas through literature analysis, stakeholder interviews, and evaluations. Key findings highlight systemic issues such as discriminatory credit decisions, opaque diagnostic algorithms, and data misuse risks. The paper concludes with actionable recommendations and policy suggestions to ensure responsible, ethical AI development, including robust governance frameworks, regular audits, and cross-sectoral accountability mechanisms.This article contributes to the growing discourse on responsible AI, offering a structured path forward for developers, policymakers, and end-users committed to deploying generative AI in a socially just and transparent manner.

本学术论文《<b>人工智能中的伦理困境：金融与医疗领域的生成式模型</b>》由斯里尼瓦桑·文卡塔拉曼、赛·马诺杰·耶勒佩迪、阿贾伊·阿库拉、文卡塔·斯里·马诺杰·博纳姆、穆罕默德·艾哈迈德以及梅西·乔伊共同撰写，批判性审视了生成式人工智能（Generative AI）模型在高风险的金融与医疗领域部署所引发的伦理挑战。诸如生成对抗网络（Generative Adversarial Networks，GANs）、变分自编码器（Variational Autoencoders，VAEs）以及基于GPT的大语言模型（Large Language Model）等技术，正日益深度融入金融服务与医疗应用场景，在欺诈检测、信用评分、疾病诊断以及药物研发等领域展现出强劲的应用能力。然而，这些技术进步也引发了诸多严峻的伦理顾虑，涉及公平性、算法偏差、数据隐私、知情同意、问责机制以及透明度等多个维度。本研究采用混合研究方法，通过文献分析、利益相关者访谈与实证评估，对技术应用场景与伦理困境展开探究。核心研究结果揭示了一系列系统性问题，包括歧视性信用决策、透明度不足的诊断算法以及数据滥用风险等。该论文最后提出了切实可行的建议与政策方案，以保障人工智能研发的负责任性与伦理合规性，其中涵盖健全的治理框架、定期审计机制以及跨部门问责体系等内容。本文为当前日益增多的负责任人工智能相关学术讨论贡献了独到视角，为致力于以社会公平且透明的方式部署生成式人工智能的开发者、政策制定者与终端用户提供了一套结构化的前行路径。