AI is quietly poisoning itself and pushing models toward collapse - but there's a cure (www.zdnet.com)

Recent discussions highlight a growing concern in the AI community about "Model Collapse," a phenomenon where AI models, particularly large language models (LLMs), degrade in performance due to being trained on their own flawed outputs—often referred to as AI slop. This cycle of deterioration, predicted to impact data governance approaches, has led analysts like those from Gartner to emphasize the urgent need for organizations to adopt a zero-trust posture toward data. As unverified AI-generated data infiltrates systems, businesses are urged to implement robust mechanisms for data authentication and verification to mitigate the risk of skewed AI outputs. To counter these challenges, Gartner recommends the appointment of dedicated AI governance leaders and the establishment of cross-functional teams that include security, data, and analytical experts. These collaborative groups will address the business risks associated with AI-generated data. Additionally, enhancements to existing data governance frameworks are suggested, focusing on real-time metadata management and validation. By recognizing these systemic issues and actively engaging in data validation, organizations can safeguard the integrity of AI applications, ensuring their usefulness well into the future.