During the 2024 Indian elections, US$50 millions was spent on authorized AI-generated content, notably
by creating deepfakes of allied (including sometimes deceased) politicians to better engage with voters,
and by translating speeches to various local languages.[182]
AI has potential benefits and potential risks.[183] AI may be able to advance science and find solutions for
serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve
everything else".[184] However, as the use of AI has become widespread, several unintended
consequences and risks have been identified.[185] In-production systems can sometimes not factor ethics
and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable
in deep learning.[186]
Machine learning algorithms require large amounts of data. The techniques used to acquire this data have
raised concerns about privacy, surveillance and copyright.
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.[187]
For example, in order to build speech recognition algorithms, Amazon has recorded millions of private
conversations and allowed temporary workers to listen to and transcribe some of them.[188] 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.[189]
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.[190] Since 2016, some privacy experts, such as Cynthia Dwork,
have begun to view privacy in terms of 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'."[191]
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".[192][193] Website owners who do not wish to have their content scraped can
indicate it in a "robots.txt" file.[194] In 2023, leading authors (including John Grisham and Jonathan
Ethics
Risks and harm
Privacy and copyright