ALGORITHMIC ETHICS IN AI-CREATED ARTWORKS - Dixit, Rajita; Patil, Jayashree; Pujari, Ketaki Anay; Puntambekar, Devendra; Parmar, Yuvraj; Kaur, Gurpreet (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The information presented in this paper explores the changing ethical issues that come with AI-generated artworks, and how algorithms, datasets, and the behavior of model-layers, cultural sensitivity, and the governance structures influence artistic results. The research evaluates various aspects of ethical risk, such as the spread of bias, stylistic theft and misrepresentation of culture, as well as manipulation of identity. Based on a multi-layered analytical framework upheld by graphs, tables, and case studies, the study points out that the main ethical vulnerabilities are due to uneven datasets, obscure semantic-layer reasoning, and inadequate protection of culturally sensitive information. To illustrate how the failures of ethics are put into practice, the paper introduces five real-world case studies that include unauthorized style mimicry, distortion of Indigenous symbols, the bias of a demographic portrait, exploitation of community heritage datasets, and manipulation of identity by a deepfake to prove how they may fail in practice. The results show that governance interventions, including consent-driven datasets, fairness auditing, cultural-protection mechanisms, and identity safeguards, can greatly mitigate the harmful output, which is achieved through their regular use. The paper ends with an overview of the future directions that focus on contextual cultural intelligence, adaptive governance, semantic interpretability and community oriented data rights. The general aim is to influence the creation of morally accountable generative AI applications, which are cognizant of creative authority, culture, and social trust.