Generative AI and the Erosion of Online Privacy
The Age of Privacy Confusion
Back in 2010, Mark Zuckerberg made a bold statement at a TechCrunch awards ceremony. He claimed that young people, particularly those who used social media, no longer cared about privacy. According to him, individuals were comfortable with sharing vast amounts of personal information openly, and this social norm was constantly evolving.
However, as time has passed, it has become clear that this statement hasn’t aged well. Privacy breaches and violations have become increasingly prevalent, leading to significant concerns about individual privacy and data protection. The common belief that individuals are solely responsible for the consequences of sharing their own information is not only persistent but also completely wrong.
In the age of generative AI, privacy breaches become even more complicated. Generative AI algorithms, such as ChatGPT, Dall-E, and Google Bard, are trained using vast amounts of data scraped without consent or notice. These algorithms absorb digital information from sources such as social media, search engines, and image hosting sites, mixing it into raw material for generative AI. The widespread adoption of generative AI across various industries poses a threat to privacy and exposes more individuals to potential harm.
The Invisible Threat: Lack of Control
Generative AI algorithms are designed to analyze and utilize massive corpora of data, including both publicly available information and data that individuals may not have knowingly provided. For instance, Dall-E trained on images collected from social media platforms and search engines, potentially incorporating individuals’ images without their knowledge or consent.
One of the most significant challenges with generative AI is the lack of transparency and control over the data used. When algorithms like ChatGPT generate inaccurate biographical information, tracing the origin of the false information becomes nearly impossible. Similarly, individuals can’t identify the sources of correct information used by the AI model. Privacy becomes a murkier concept when personal data is utilized without individuals’ knowledge or control.
Data Brokers and the Amplification of Privacy Risks
Data brokers, historically renowned for crawling the web and compiling massive dossiers on individuals, have posed threats to privacy. However, their results were not freely available to the average person or integrated into common search engines and word processors.
Generative AI amplifies these privacy risks by making the data mix widely accessible. It is difficult to determine the exact contents of training sets used by generative AI algorithms. The use of public records, news articles, employee biographies, and even photos and videos collected from various online sources creates a complex digital dossier on individuals.
For example, if you were accidentally captured in a photo posted on Flickr in 2007, your image may have been used to train a generative AI algorithm without your knowledge. The lack of awareness and control over how personal information is utilized in generative AI models raises serious privacy concerns.
Privacy in a Networked Society
Anthropologists and legal scholars have long recognized that privacy cannot be solely controlled by individuals, as information is shared within networks. People communicate and exchange information both online and offline, making it challenging to limit the spread of private information. Even if individuals ask their friends not to post certain content or mention them on social media, their privacy is only as secure as their most talkative contact.
Online privacy breaches often occur when information shared within one context is passed on to another party and interpreted differently. TikTok videos created for specific audiences can be taken out of context and repurposed for completely different purposes. Political speeches aimed at sympathetic audiences can be seen as shocking when viewed by the opposition.
New technologies further exacerbate online privacy concerns. Forensic genealogy, for instance, enables law enforcement agencies to identify suspects by analyzing genetic evidence from distant relatives. Even if individuals choose not to use platforms like Ancestry.com, they cannot prevent their relatives or acquaintances from utilizing such services, potentially implicating them in privacy invasions.
Big Data, another technology that relies on extensive datasets, frequently implicates friends, relatives, and distant acquaintances. When integrated into predictive surveillance or risk assessment algorithms, it can have substantial implications for privacy. Ultimately, there is little individuals can do to prevent these invasions of privacy once their data is intertwined with the vast web of information.
The Uncharted Terrain of Generative AI
Generative AI brings new challenges to the concept of privacy and the ability to maintain an acceptable level of control over personal information. The results produced by generative AI algorithms detach completely from their original sources, leading to unforeseen consequences.
Leaking private text messages can have significant ramifications, but when the entirety of platforms like Reddit becomes material for robot-generated poetry and content, the implications are far-reaching. Information initially provided within a specific context can be entirely recontextualized and remixed, changing its meaning and violating what philosopher Helen Nissenbaum refers to as “contextual integrity.”
In this era of generative AI, individual attempts to regulate or safeguard personal information become increasingly futile. The erosion of control over our own data raises concerns about privacy and the ethical implications of utilizing generative AI algorithms on a large scale.
Expanding Perspectives on Online Privacy
To fully comprehend the implications and complexities of online privacy in the age of generative AI, it is essential to delve deeper into related concepts and explore unique perspectives:
The Changing Definition of Privacy
Privacy is no longer just about the individual control over personal information. It has transformed into a shared responsibility that extends beyond individual actions. The rapid advancement of technology has challenged traditional notions of privacy and necessitates a collective effort to protect personal data in a networked society.
The Fine Line Between Innovation and Ethics
While generative AI offers incredible potential for innovation, it also poses ethical dilemmas regarding privacy. Striking a balance between technological advancement and individual rights to privacy is crucial in shaping the future of generative AI.
Rethinking Data Ownership
Generative AI exposes the flaws in the current model of data ownership. As individuals, it is increasingly challenging to determine the extent of control we have over our own data. Exploring alternative frameworks for data ownership and establishing clear guidelines and regulations become imperative.
Mitigating the Risks: Technological and Legal Solutions
The risks associated with generative AI and privacy breaches can be mitigated through a combination of technological advancements and robust legal frameworks. Methods such as differential privacy, federated learning, and transparent AI algorithms hold promise in protecting user data while allowing for innovation.
Conclusion
The advent of generative AI has ushered in a new era of privacy concerns. Traditional models of individual responsibility for privacy are no longer adequate in a world where algorithms scrape data without consent or notice and generate content detached from its original sources. Privacy becomes a shared responsibility, and navigating the complex web of privacy threats requires both technological advancements and comprehensive legal frameworks.
As individuals and society at large, it is crucial to engage in ongoing discussions surrounding privacy rights and the implications of generative AI. By understanding the shifts in privacy dynamics, we can collectively work towards striking a balance between technological innovation and protecting our fundamental rights to privacy in the digital age.
Summary: The introduction of generative AI has challenged traditional notions of privacy. Mark Zuckerberg’s statement about young people’s disregard for privacy in 2010 has since proven to be inaccurate. Generative AI algorithms scrape vast amounts of data without consent, making it impossible for individuals to know how and where their information is used. Privacy violations often occur when information is shared within networks, and new technologies like forensic genealogy further compromise privacy. Generative AI exacerbates privacy risks by utilizing vast datasets and remixing information out of context. To navigate this evolving landscape, it is crucial to explore different perspectives on privacy and consider technological and legal solutions to mitigate risks.
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In 2010, Mark Zuckerberg told the audience at a TechCrunch awards ceremony that young people, especially social media users, no longer cared about privacy. “People have really felt comfortable not only sharing more information and of different kinds, but also more openly and with more people,” he said. “That social norm is simply something that has evolved over time.” While this statement obviously hasn’t aged well, it reflects a common belief that privacy breaches occur when people reveal their own information. In other words, when something posted on Reddit or TikTok goes viral, or a nude photo sent to a fan gets leaked, it’s first and foremost the fault of the person who posted it. This model of individualized responsibility is very persistent. It is also completely wrong. And it is irrelevant in the age of generative AI.
Generative AI completely eliminates the idea of individual responsibility for privacy because you can’t control these algorithms’ access to your information or what they do with it. Tools like ChatGPT, Dall-E and Google Bard are data trained scraping without consent, or even notice. At worst, training sets absorb vast amounts of digital information and combine it into a data mix that serves as raw material for generative AI. As tech companies strive to embed generative AI into every imaginable product, from search engines to games to military devices, it’s impossible to know where this result is headed or how it might be interpreted. Your predecessors violating privacy, data brokersThey also crawled the web and assembled massive dossiers on individuals, but their results are not available to the average person, for free or integrated into search engines and word processors. The widespread availability of generative AI exacerbates potential privacy violations and exposes more people to harmful consequences.
The massive corpora employed by generative AI inevitably contain information about people that was not provided, created, or even known to be available. Public records on marriages, mortgages, and voter registration are valid, as are news, employee biographies, and Wikipedia pages. But the porridge also contains millions of photos and videos; Dall-E, for example, trained on images collected from social media, search engines, and image hosting sites. So if you’re in the background of a 2007 Flickr shot, your image could be used to train an algorithm. No one seems to know what the data mix contains and there is no way to monitor or control it. When ChatGPT writes an inaccurate bio of me, I don’t know where the false information originated, but I also don’t know where the correct information came from. We’re used to thinking of privacy as individual control over information, but it’s impossible to regulate how your personal information is used if you don’t even know where it came from.
Anthropologists and legal scholars have known for years that privacy cannot be controlled by individuals, in part because we share information within networks. In other words, people talk to each other, both on and offline. There is no easy way to put limits on that; You can ask your friends not to post photos of your kids on Instagram or mention you on TikTok, but you’re only as private as your most talkative contact. Online privacy violations often occur because information provided in an environment with particular standards and expectations is passed on to another party and interpreted differently. TikToks created for queer and progressive audiences become fodder for anti-trans campaigns; Political speeches delivered before sympathetic audiences look shocking when viewed by the opposition.
New technologies increasingly compromise this online privacy. Forensic genealogy, for example, allows police to identify suspects by examining genetic evidence collected from distant relatives. You can choose not to use Ancestry.com, but you can’t stop a third cousin (who you probably don’t even know exists) from doing the same. Big Data, which uses massive data sets in a similar way, frequently implicates friends, relatives, and even distant acquaintances, which becomes extraordinarily worrisome when integrated into predictive surveillance or risk assessment algorithms. There is nothing that people can do to prevent such invasions of privacy.
Generative AI raises these online privacy concerns. It compromises our ability to do “privacy work,” the methods and strategies we all employ to maintain an acceptable level of privacy. And the results of generative AI are completely separated from their original source in ways previously unimaginable. It’s one thing to leak private text messages, it’s quite another for all of Reddit to be used as material for robot poetry and bad college jobs. Information provided in a context can be completely recontextualized and remixed, changing its meaning and violating what the philosopher Helen Nissenbaum calls “contextual integrity”. How can a single person avoid this?
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