Artificial intelligence algorithms need big quantities of data. The methods utilized to obtain this data have raised issues about personal privacy, security and copyright.

AI-powered devices and services, such as virtual assistants and IoT products, continuously gather individual details, raising issues about invasive data gathering and unapproved gain access to by 3rd parties. The loss of personal privacy is more exacerbated by AI's ability to process and combine vast amounts of information, possibly leading to a security society where private activities are constantly kept an eye on and evaluated without adequate safeguards or transparency.

Sensitive user information collected may consist of online activity records, geolocation information, video, or audio. [204] For instance, in order to construct speech acknowledgment algorithms, Amazon has taped millions of private discussions and allowed short-term employees to listen to and transcribe some of them. [205] Opinions about this widespread security range from those who see it as a required evil to those for whom it is plainly unethical and a violation of the right to privacy. [206]
AI designers argue that this is the only method to provide important applications and have actually established several techniques that attempt to maintain personal privacy while still obtaining the information, such as data aggregation, de-identification and differential privacy. [207] Since 2016, some privacy specialists, such as Cynthia Dwork, have begun to see personal privacy in terms of fairness. Brian Christian composed that professionals have actually pivoted "from the concern of 'what they know' to the question of 'what they're finishing with it'." [208]
Generative AI is typically trained on unlicensed copyrighted works, including in domains such as images or computer system code