Recent studies reveal that artificial intelligence models, fueled by large amounts of public data, can infer confidential information such as gender, location, and age from seemingly innocuous interactions. This capability enables scammers to access private details of unsuspecting users, turning online conversations into a valuable source of sensitive data without people realizing they have lost their anonymity. The risk lies in the training process of these systems, which assimilate conversations and posts from the internet, including personal details that users shared years ago. The AI combines these subtle clues to deduce information that individuals did not explicitly disclose in the current context, demonstrating that online privacy is vulnerable even to casual questions or comments in forums and chats. This is relevant to open data because it illustrates the fragility of anonymity when public data is combined with advanced algorithms. By democratizing access to large volumes of information, there arises a critical need to implement ethical and technical safeguards that prevent the re-identification of individuals. The article emphasizes that transparency in data handling is insufficient if protection against privacy inference is not prioritized—a central challenge for the responsible governance of open information.
Source: invdes.com.mxPublished on 2023-10-20
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