Artificial intelligence (AI) is transforming industries, governance, and daily life, but its rapid adoption raises significant ethical and regulatory challenges. In this paper, Aarav Kulshrestha explores the intersections of bias, copyright, liability, and security in AI systems, analyzing real-world cases such as generative art copyright disputes, algorithmic bias in chatbots, and AI-assisted military technologies. Using qualitative policy analysis and case study methodology, the research identifies structural gaps in existing legal and regulatory frameworks that leave creators, users, and society vulnerable. Findings highlight the need for standardized yet flexible approaches, including compulsory licensing for copyrighted data, algorithmic impact assessments, tiered liability frameworks, and dual-use security regulations. By proposing integrated and globally coordinated policies, the thesis demonstrates how responsible AI development can balance innovation with accountability, equity, and security, providing a blueprint for ethical AI governance.
Bias, Copyright, and Liability Policy
Corresponding Author: Aarav Kulshrestha*
Supervisor: Dr Jonathan Kenigson, FRSA
