Ethical Dimensions of Generative AI: Bias, Misuse, and Responsibility
Abstract
As Generative AI models grow in complexity and capability, ethical challenges surrounding their deployment become increasingly critical. This paper examines the ethical landscape of GenAI, focusing on issues such as algorithmic bias, deepfake misuse, data privacy, and accountability. We present case studies highlighting real-world implications and propose guidelines for responsible design, development, and governance of generative systems. The study also introduces an ethical audit checklist for organizations deploying GenAI in public-facing applications.
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