Editing Artificial intelligence (section)

==== Privacy and copyright ====
{{Further|Information privacy|Artificial intelligence and copyright}}

Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about [[privacy]], [[surveillance]] and [[copyright]].

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AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency.

Sensitive user data collected may include online activity records, geolocation data, video, or audio.{{Sfnp|GAO|2022}} For example, in order to build [[speech recognition]] algorithms, [[Amazon (company)|Amazon]] has recorded millions of private conversations and allowed [[temporary worker]]s to listen to and transcribe some of them.{{Sfnp|Valinsky|2019}} Opinions about this widespread surveillance range from those who see it as a [[necessary evil]] to those for whom it is clearly [[unethical]] and a violation of the [[right to privacy]].{{Sfnp|Russell|Norvig|2021|p=991}}

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AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as [[data aggregation]], [[de-identification]] and [[differential privacy]].{{Sfnp|Russell|Norvig|2021|pp=991–992}} Since 2016, some privacy experts, such as [[Cynthia Dwork]], have begun to view privacy in terms of [[fairness (machine learning)|fairness]]. [[Brian Christian]] wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."{{Sfnp|Christian|2020|p=63}}

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Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "[[fair use]]". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work".{{Sfnp|Vincent|2022}}<ref>{{Cite web |last=Kopel |first=Matthew |title=Copyright Services: Fair Use |url=https://guides.library.cornell.edu/copyright/fair-use |access-date=2024-04-26 |website=Cornell University Library |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926194057/https://guides.library.cornell.edu/copyright/fair-use |url-status=live }}</ref> Website owners who do not wish to have their content scraped can indicate it in a "[[robots.txt]]" file.<ref>{{Cite magazine |last=Burgess |first=Matt |title=How to Stop Your Data From Being Used to Train AI |url=https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai |access-date=2024-04-26 |magazine=Wired |issn=1059-1028 |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003180100/https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai/ |url-status=live }}</ref> In 2023, leading authors (including [[John Grisham]] and [[Jonathan Franzen]]) sued AI companies for using their work to train generative AI.{{Sfnp|Reisner|2023}}{{Sfnp|Alter|Harris|2023}} Another discussed approach is to envision a separate ''[[sui generis]]'' system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.<ref>{{Cite web |title=Getting the Innovation Ecosystem Ready for AI. An IP policy toolkit |url=https://www.wipo.int/edocs/pubdocs/en/wipo-pub-2003-en-getting-the-innovation-ecosystem-ready-for-ai.pdf |website=[[WIPO]]}}</ref>