Top three privacy issues in AI - exposure of prompts, lack of privacy in custom AI models, use of private data to train AI systems.

Top three privacy issues in AI - exposure of prompts, lack of privacy in custom AI models, use of private data to train AI systems.

AI (Artificial Intelligence) has generated frenetic excitement among both consumers and businesses - driven by the belief that LLMs (Large Language Models) and tools like ChatGPT will transform the way we study, work and live. However, there are privacy concerns as many users do not consider how their personal information is used and what impact this might have on their privacy.

There have been countless examples of AI data breaches. In March 2023, OpenAI temporarily took ChatGPT offline after a “significant” bug resulted in users being able to see strangers’ conversation histories. The same error resulted in subscriber payment information, including names, email addresses and partial credit card numbers, becoming publicly available.

In September 2023, 38 terabytes of Microsoft data was accidentally published by an employee, causing cybersecurity experts to warn that attackers could inject malicious code into AI models. Researchers have also been able to manipulate AI systems to reveal confidential records. These data breaches highlight the challenges AI must overcome to become a reliable and trustworthy force in our lives.

Another problem is the lack of transparency in AI systems. Gemini, Google's chatbot, openly admits that all conversations are reviewed by human reviewers. There is concern that information fed into AI systems could be repurposed and distributed to a wider audience. Companies like OpenAI are already facing several lawsuits alleging that their chatbots were trained on copyrighted material.

Another privacy issue is that custom AI models trained by organizations are not completely private when they exist within platforms like ChatGPT. There is no way to know whether inputs are being used to train these massive systems or whether personal information could be used in future models.

Another concern is that private data is used to train AI systems. There is a fear that AI systems have derived their intelligence from countless websites. For some of these sources, it could be argued that the owners of this information had a reasonable expectation of privacy.

It is important to note that AI is already having a strong impact on our daily lives. Many tools and apps that we use every day are already heavily influenced by AI and react to our behavior. This presents both opportunities and risks in the area of ​​data protection.

To protect privacy in AI, decentralization could play an important role. Decentralized physical infrastructure networks (DePINs) can ensure that users can fully take advantage of the benefits of AI without compromising their privacy. Encrypted inputs can provide more personal results, while privacy LLMs could ensure users have full control over their data at all times and are protected from misuse.

However, there is a risk that regulators will not be able to keep up with the breakneck speed of the AI ​​industry. Therefore, consumers need to protect their own data and monitor how it is used. Lessons must also be learned from the data protection scandals of recent years.

Overall, AI will have an indelible impact on all of our lives in the coming years. However, it is crucial that privacy issues are addressed to maintain trust in the technology while taking full advantage of its benefits.