Artificial intelligence (AI) has increasingly relied on vast and diverse datasets to train models. However, a major issue has arisen regarding these datasets’ transparency and legal compliance. Researchers and developers often use large-scale data without fully understanding its origins, proper attribution, or licensing terms. As AI continues to expand, these data transparency and licensing gaps pose significant ethical and legal risks, making it crucial to audit and trace the datasets used in model development. The central problem is the frequent use of unlicensed or improperly documented data in AI model training. Many datasets, especially those used for fine-tuning AI models, come from sources that do not provide clear licensing information. This results in high rates of misattribution or non-compliance with data usage terms. The risks associated with such practices […]
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