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<br>Artificial intelligence algorithms need big amounts of data. The methods used to obtain this information have actually raised concerns about personal privacy, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11926756) surveillance and copyright.<br>
<br>AI-powered devices and services, such as virtual assistants and IoT products, constantly gather individual details, raising concerns about invasive information gathering and unauthorized gain access to by 3rd parties. The loss of personal privacy is further worsened by AI's capability to procedure and integrate vast amounts of information, possibly causing a surveillance society where private activities are continuously kept an eye on and examined without sufficient safeguards or transparency.<br>
<br>Sensitive user information gathered might consist of online activity records, geolocation information, video, or audio. [204] For example, in order to construct speech recognition algorithms, Amazon has actually tape-recorded millions of personal conversations and allowed temporary employees to listen to and transcribe a few of them. [205] Opinions about this widespread security range from those who see it as a necessary evil to those for whom it is plainly unethical and an offense of the right to personal privacy. [206]
<br>AI developers argue that this is the only method to provide valuable applications and have established several methods that attempt to maintain privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. [207] Since 2016, some privacy professionals, such as Cynthia Dwork, have started to see privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they understand' to the question of 'what they're finishing with it'." [208]
<br>Generative [AI](https://ozgurtasdemir.net) is typically trained on unlicensed copyrighted works, including in domains such as images or computer code
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