Ethical Challenges and Accountability in Generative AI: Managing Copyright Violations and Misinformation in Responsible AI Systems

Abstract

Generative AI can produce content that violates copyright law and spreads misinformation. This essay raises the question of how legal frameworks and industry discussions about supervision, liability, and accountability in AI can serve as a guide in the development of responsible AI systems that manage these challenges while ensuring that society can use generative AIs. What underlying challenges of generative AI suggest consideration of accountability and ethical guidance? It explores how these questions intersect with concerns around fake news, with a focus on potentially affected stakeholders. In doing so, it needs to evaluate and connect conceptual work on the relation between control and responsibility in AI with existing technology-specific suggestions for addressing challenges of AI that create problems for others.

This essay discusses the emergence of generative AI as potentially harmful AI. It focuses on two legal and social challenges that supposedly become aggravated through the exponential quality and output of generative AI rather than its outputs per se, which can often be caught with the tools devised for detection in traditional AI. In the first part, the essay tries to find other theoretical and practical current developments as a guide to generate necessary ethical frameworks or guide understanding legislation. The second part links these challenges to existing scholarly debates of generative AI or AI-induced problems in general. It ends by pointing out the role of intention and cultural norms as useful categories to socially detect and legally punish harmful behavior in the AI realm. The emergence of sophisticated enough generative AI tools is a fairly recent trend that is continuously exposing underlying intellectual property concerns. Generative AI creates, at an antithetical level, an asymmetry of content repacking by allowing experts to outwardly imitate emerging trends, but the general population, or a more time-expensive sampling of the population through replicable methods.