How AI Learning Models Are Trapped by Their Own Data
from AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence Hosted by Fexingo
In this episode of AI Ethics with Fexingo, Lucas and Luna explore a surprising source of bias: when AI models are trained on data generated by older AI systems. They dive into a 2025 study from researchers at Oxford and MIT showing that language models fed synthetic text from earlier models develop 'model collapse'—becoming less diverse and more error-prone over generations. Lucas explains why this matters for everything from customer service chatbots to medical diagnostic tools, and Luna pushes him on whether this is a fixable technical problem or a fundamental limit. They also discuss the proposed 'data provenance' tagging system some companies are experimenting with to label AI-generated content in training sets. This episode ends with the hosts reflecting on how the solution might ironically require more human oversight, not less. #AIEthics #ModelCollapse #BiasInAI #SyntheticData #DataProvenance #OxfordStudy #MITStudy #LanguageModels #MachineLearning #AISafety #ResponsibleAI #Tech #ArtificialIntelligence #DataQuality #GenerativeAI #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo
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