ARTIFICIAL INTELLIGENCE SURVEILLANCE

Researchers at Oxford, Imperial College London, and UCLouvain developed a new mathematical model to assess the risks of AI identification techniques, particularly concerning privacy violations. This model uses Bayesian statistics to evaluate identification accuracy across various scales, improving upon previous methods. The model's application includes online tracking and high-stakes environments like healthcare and border control. The improved accuracy helps organizations balance the benefits of AI with data protection needs. The study, published in Nature Communications, offers a crucial tool for regulators and researchers to mitigate privacy risks associated with increasingly sophisticated AI identification technologies.
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