Sketchy Imbalances In Data Training Are Distorting AI-Generated Mental Health Guidance
The article discusses the imbalances in data training for AI, particularly in the context of mental health guidance. It highlights how the data used to train AI models can be skewed, leading to potentially harmful advice. Users may mistakenly believe that AI-generated guidance is comprehensive and authoritative, despite its inherent biases.
- ▪AI training often relies on vast amounts of internet data, which can be imbalanced.
- ▪This imbalance can lead to inadequate mental health advice being generated by AI.
- ▪Users may not be aware of these biases, assuming the AI is fully authoritative.
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InnovationAISketchy Imbalances In Data Training Are Distorting AI-Generated Mental Health GuidanceByLance Eliot,Contributor.Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant.Follow AuthorMay 23, 2026, 03:15am EDT--:-- / --:--This voice experience is generated by AI. Learn more.This voice experience is generated by AI. Learn more.We must overcome the imbalance that occurs when AI is initially trained, especially when it comes to the AI providing mental health guidance.gettyIn today’s column, I examine a crucial weakness in most of the contemporary generative AI and large language models (LLMs) concerning the data and knowledge they are being trained on, especially in the mental health domain.
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