Ethical Dilemmas in AI: Generative Models in Finance and Healthcare

This research article, "Ethical Dilemmas in AI: Generative Models in Finance and Healthcare" authored by Srinivasan Venkataramanan, Sai Manoj Yellepeddi, Ajay Aakula, Venkata Sri Manoj Bonam, Mohammed Ahmed, and Meccy Joy, 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.