Does ChatGPT compromise your privacy? Find out more…
In the world of AI and machine learning, there is a growing buzz surrounding language learning models. These tools, such as ChatGPT-3, have gained immense popularity due to their ability to answer questions and even generate code. They have a wide range of applications, including chatbots, language translation, and text summarization. However, as with any technology, there are concerns and potential drawbacks.
One of the major concerns with these models is privacy. It can be difficult for individuals to determine if their data has been used to train a machine learning model. For example, GPT-3 is a large language model that has been trained on a vast amount of internet data, including personal websites and social media content. This raises concerns that the model may utilize people’s data without their consent and that controlling or deleting the data used to train the model may be challenging.
Another issue is the right to be forgotten. As the use of GPT models and other machine learning models becomes more widespread, people may want to have the ability to remove their data from the model.
People are furious that data is being used without their permission, explains Sadia Afroz, an AI researcher at Avast. Sometimes, people have deleted their data, but since the language model has already used it, the data remains. They don’t know how to delete it.
Currently, there is no widely accepted method for individuals to request the removal of their data from a machine learning model once it has been used to train the model. Some researchers and companies are working on methods to allow for the removal or forgetting of specific data points or user information, but these methods are still in their early stages of development. Additionally, there are technical challenges associated with removing data from machine learning models, as the data may have been integral to training the model, and its removal could compromise accuracy.
The legality of using personal data to train machine learning models, such as GPT-3, can vary depending on the specific laws and regulations of a given country or region. In the European Union, for instance, the General Data Protection Regulation (GDPR) governs the use of personal data and requires that data be collected and used only for specific, lawful purposes.
GDPR is heavily focused on purpose restriction, says Afroz. You must use the data for the purpose you collected it for. If you want to use it for something else, you have to obtain permission. But language models are the opposite of that – the data can be used for any purpose. How can GDPR enforce this restriction?
Under GDPR, organizations must obtain explicit consent from individuals before collecting and using their personal data. While there is a legal basis for processing personal data for scientific and historical research, the controller must adhere to the principles and rights outlined in the GDPR. These rights include the right to be informed, right of access, right to rectification, right to erasure, right to object, and right to data portability. Given this context, it appears that language learning models do not fully comply with GDPR, which could pose a significant barrier to their future growth.
In the United States, there is no federal law specifically regulating the use of personal data to train machine learning models. However, organizations generally need to comply with laws such as the Health Insurance Portability and Accountability Act (HIPAA) and the Children’s Online Privacy Protection Act (COPPA) if they collect and use personal data from individuals in sensitive categories. In California, where most major tech companies are based, companies must adhere to the California Consumer Privacy Act (CCPA), which has similar privacy requirements to GDPR.
It is worth noting that the field of AI models, including GPT-3, is ever-evolving. Consequently, laws and regulations surrounding the use of personal data in AI are likely to change, making it crucial to stay updated on the latest legal developments in this area.
Another significant concern surrounding GPT models is the issue of misinformation and lack of verification. These models are known to present information confidently but inaccurately. This lack of fact-checking can potentially lead to the spread of false information, which is particularly dangerous in fields like news and politics. Google, for example, plans to employ large language learning models to enhance customer service, but it remains unclear how they will address the crucial element of fact-checking.
While large language learning models have the potential to revolutionize our interaction with technology and automate various tasks, it is crucial to consider their potential drawbacks and address the associated concerns. As the use of these models becomes more widespread, addressing privacy concerns and finding solutions for the right to be forgotten become paramount.
