Establishing Global AI Accountability: Training Data Transparency, Copyright, and Misinformation

Abstract

As artificial intelligence (AI) technologies continue advancing at a rapid pace, the systems' growing capabilities as well as their expanding integration into vital social functions are raising complex questions around trust and accountability. AI models like large language models are increasingly opaque black boxes, providing limited visibility into critical details such as the training data used to develop them. Meanwhile, issues around potential copyright infringement, factual accuracy, and the generation of misinformation currently lack effective guardrails and best practices, even as AI is deployed in sensitive areas like healthcare, education, finance, and other domains with significant public impact. This paper analyzes three key ethical dimensions around contemporary AI systems—transparency, intellectual property protection, and information quality—arguing that establishing global accountability frameworks to govern these areas is essential as AI use accelerates worldwide. The background provides an overview of common training data development practices, highlighting how reliance on limited sources like Wikipedia and lack of scrutiny over training datasets can propagate inaccuracies and biases into AI systems. Core problems analyzed include the risk of unreliable results from questionable data sources, financial harms to content creators from copyright infringement, and dangers of algorithmically generated misinformation spreading quickly through social channels. To balance continued AI innovation with appropriate ethical safeguards and oversight, the paper suggests mandating transparency into the precise training data and methodologies used to develop AI systems intended for public or commercial use. Implementing standardized global licensing agreements around copyrighted materials used to train models could provide fair compensation for content creators while enabling access to higher-quality datasets. And enacting procedures to test outputs for factual correctness and track the provenance of questionable information back to the responsible party offers one avenue to minimize harmful misinformation emerging from AI systems. With careful coordination across stakeholders from government, research, industry, and civil society, standards like these may establish reasonable accountability baselines to match AI's rapidly evolving capabilities. Action is urgent, however, as public trust depends heavily on demonstrating that equitable frameworks to manage these risks are keeping pace.