Title: Is the Use of People’s Data by ChatGPT Legal?
In the world of artificial intelligence and machine learning, language learning models have gained immense popularity. One of the most widely recognized tools in this category is ChatGPT-3, a remarkable language model capable of answering questions and generating code. These models find applications in chatbots, language translation, and text summarization. Despite their broad usability, there are concerns and potential drawbacks associated with these models.
Privacy is a significant concern surrounding language learning models. Users often find it difficult to determine whether their personal data has been incorporated into machine learning algorithms. For example, GPT-3 is a large language model trained using a vast amount of internet data, including personal websites and social media content. This raises concerns that the model may utilize individuals’ data without proper consent, and it becomes challenging to control and delete the data used for training.
An additional concern relates to the right to be forgotten. As the use of GPT models and similar machine learning models becomes more widespread, individuals may desire the ability to erase their data from these models.
Sadia Afroz, an AI researcher with Avast, highlighted people’s frustration regarding data usage without permission. Deleting personal data after it has been trained by language models is ineffective as the data remains within the model indefinitely. Unfortunately, there is currently no established method for individuals to request the removal of their data from a machine learning model. Scholars and companies are working on potential solutions, but they are still in the nascent stages of development. Furthermore, removing data from machine learning models presents technical challenges, as removing vital data may result in reduced model accuracy.
The legal implications of utilizing personal data to train machine learning models like GPT-3 vary depending on specific country or regional laws and regulations. In the European Union, for instance, the General Data Protection Regulation (GDPR) governs the use of personal data, necessitating that data be collected and used solely for specific lawful purposes.
Sadiah Afroz points out the contradiction between the GDPR’s purpose restriction and the flexible usage of data in language models. Language models can employ personal data for diverse purposes, making it difficult for GDPR to impose strict restrictions.
Under the GDPR, organizations must obtain explicit consent from individuals before collecting and utilizing their personal data. While there are legal grounds for processing personal data for scientific and historical research, the controller must comply with GDPR principles and rights, including the right to be informed, right of access, right to rectification, right to erasure, right to object, and right to data portability. It appears that the operation of language learning models contradicts GDPR regulations, potentially impeding their future growth.
In the United States, there is no federal law specifically governing the use of personal data to train machine learning models. However, organizations must generally comply with laws like the Health Insurance Portability and Accountability Act (HIPAA) and the Children’s Online Privacy Protection Act (COPPA) when collecting and utilizing personal data from individuals falling within sensitive categories. In California, where most tech companies are located, the California Consumer Privacy Act (CCPA) requires companies to adhere to privacy requirements similar to GDPR.
The field of AI model development, such as GPT-3, is continuously evolving. Consequently, laws and regulations surrounding personal data usage in AI are expected to change over time. Staying updated on the latest legal developments in this area is essential.
Another significant concern regarding GPT models is the prevalence of misinformation due to inadequate fact-checking. These language learning AIs often present information with confidence but may not always be accurate. The lack of fact-checking can contribute to the spread of false information, especially in critical areas like news and politics. While companies such as Google plan to utilize large language learning models to enhance their services, managing fact-checking processes remains an ongoing challenge.
While large language learning models possess the potential to revolutionize our interaction with technology and automate various tasks, it is essential to address the associated privacy concerns and develop appropriate solutions for the right to be forgotten issue.
