
The AI Data Paradox: Negotiating Tensions Between User Privacy and Comprehensive Training Data
The true potential of Generative Artificial Intelligence (GenAI) can be realized when the datasets they are trained on reflect the breadth of human knowledge and experience. In direct contrast to this vision, is the demand for personal data privacy. User-facing GenAI platforms can function as closed systems that cannot be used to further train the AI models. This results in a data paradox in which the AI models retain the homogeneity of initial training datasets. This presentation offers a critical examination of the AI paradox – the tension between protecting individual user data and the need for heterogenous training datasets to reduce bias and improve model robustness.
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