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GDPR and CCPA: Implications for Large Language Models

Updated: Mar 7


The General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States are comprehensive data protection laws that aim to give individuals control over their personal data. As these regulations continue to evolve, they pose significant implications for various entities engaged in the development and application of Large Language Models (LLMs). This article aims to shed light on the possible issues for companies developing these models, open-source LLM projects, and companies building products using these models.



Companies Developing Large Language Models


Data Collection and Processing: Under both GDPR and CCPA, companies are required to obtain explicit consent from individuals before collecting and processing their personal data. For companies developing LLMs, this means they must ensure that the data used for training their models has been collected with the necessary consent. In scenarios where companies use online user-generated data, ensuring consent can be challenging. For instance, if a company uses publicly available social media posts, blog articles, or forum discussions to train their LLM, they could potentially infringe upon the rights of the individuals who created those posts or articles. Even though the information may be publicly accessible, using it for purposes other than those initially intended (like training an LLM) without explicit consent may breach GDPR and CCPA regulations.


Right to Erasure: Both GDPR and CCPA provide individuals with the 'right to erasure' or 'right to be forgotten'. This right allows individuals to request that companies delete their personal data. This poses a unique challenge for companies developing LLMs, as once the data has been used to train a model, it becomes part of the model's learned parameters and cannot be easily extracted or deleted. For example, if a company has used thousands of online posts to train its LLM, and one individual requests the removal of their data, the company would need to retrain the entire model without that individual's data. The computational cost of this could be substantial, and it may not even be feasible if the company cannot identify which data in the model belongs to the individual making the request.


Open Source Large Language Model Projects


Data Anonymization: Open-source projects typically rely on contributions from a wide range of individuals and organizations. For LLM projects, this often means using diverse datasets for training. Anonymizing these datasets to ensure they don't contain personal data can be a significant challenge. For instance, if an open-source LLM project uses contributions from social media, they need to ensure that all data is thoroughly anonymized, removing all personally identifiable information (PII) before using it for training. This process can be labor-intensive and require sophisticated techniques to ensure complete anonymization.


Accountability and Compliance: With numerous contributors and a decentralized nature, ensuring compliance with GDPR and CCPA can be complex for open-source projects. This is particularly true for LLM projects where data contributors may be located across different jurisdictions, each with its own data protection laws. For example, an open-source LLM project might have contributors from Europe, the United States, and Asia. Each of these regions has its own data protection regulations, and ensuring compliance with all of them can be a logistical nightmare. The project would need to have clear guidelines and processes in place to ensure that all contributors comply with relevant regulations, which could be a significant administrative burden.


Companies Building Products Using Large Language Models


Transparency and Explainability: Companies using LLMs in their products must ensure transparency in how they use and process data. This includes providing clear information about how the models work, what data they use, and how they make decisions. Both GDPR and CCPA require companies to provide such information to consumers. However, LLMs are inherently complex, and explaining their operations and decision-making processes in simple, understandable terms can be a major challenge. LLMs generate outputs based on learned patterns from vast amounts of data, and the exact path from input to output isn't always clear, even to the model's developers. This 'black box' nature of LLMs can make it difficult to comply with regulations requiring transparency and explainability.


Data Minimization: The principle of data minimization, as outlined in both GDPR and CCPA, requires companies to only collect and process the minimum amount of data necessary to fulfill their stated purpose. For LLMs, which typically require large volumes of data for effective training, adhering to this principle can be challenging. For example, a company might use an LLM for customer support, where the model needs to understand a wide range of queries and provide appropriate responses. To do this effectively, the company might need to train the model on extensive customer interaction data, which may be seen as excessive under data minimization principles. The company would need to carefully balance the model's performance needs with regulatory requirements, which could involve complex decisions about the volume and nature of data used for training.


Other Relevant Regulations


In addition to GDPR and CCPA, there are a variety of other regulations worldwide that can significantly impact the development, use, and application of Large Language Models (LLMs). Here are a few notable ones:


California Privacy Rights Act (CPRA): The CPRA, an extension of the CCPA, will come into effect on January 1, 2023. This act includes stricter amendments such as additional obligations to employees, including the right to rectification, portability, and the right to limit the use and disclosure of sensitive personal information​​.


China's Personal Information Protection Law (PIPL): Implemented in November 2021, PIPL shares some similarities with GDPR, such as the right to access, withdrawal, and deletion. However, it also has unique requirements, such as the obligation for organizations to store the data collected from Chinese subjects within China. Moreover, processing this data outside of China requires review and approval by national security agents​.


Virginia’s Consumer Data Protection Act (CDPA) and the Colorado Privacy Act (CPA): These laws require qualifying organizations to provide a universal opt-out process. Further technical guidance from both Virginia and Colorado is expected in 2023. Other states like Utah and Connecticut have followed with their own comprehensive data laws​​.


EU Data Governance Act (DGA) and EU Data Act: The DGA aims to facilitate data access and sharing with the public sector for the public good. The Data Act, on the other hand, aims to provide greater transparency to data subjects and provide the public sector with more access to useful private sector data, especially in emergency situations. The EU is also proposing the Artificial Intelligence (AI) Act, which would categorize AI applications into risk categories​​.


Schrems II and Cross-border Data Transfers: This is a ruling by the Court of Justice of the European Union (CJEU) that invalidated the EU-US Privacy Shield Framework, a mechanism that was used to facilitate data transfers between the EU and the US. As a result, many organizations may have to reassess their processes for handling international data transfers. The European Data Protection Board (EDPB) recommends conducting Transfer Impact Assessments (TIAs) to ensure compliance​.


It's important to note that the impact of some of these regulations specifically on LLMs is still undefined. However, given that these laws generally apply to how personal data is collected, processed, and transferred, they are likely to have significant implications for the development and use of LLMs. Also, the regulatory landscape is continually evolving, with new data protection laws being proposed and existing ones being updated. Therefore, entities engaged in the development or application of LLMs should continuously monitor changes in these regulations and seek legal advice as necessary to ensure compliance.


The Future of Large Language Models and Data Protection Regulations


With the continued evolution of data protection laws and the rapid advancement of LLMs, the intersection of these two areas will remain a dynamic and challenging field. It will require ongoing vigilance from all stakeholders, including companies developing or using LLMs, open-source projects, and regulators. In particular, the development and application of LLMs will need to become more transparent and accountable. This could involve developing new techniques for data anonymization, improving methods for explaining the workings of LLMs, and implementing more robust processes for data consent and erasure.


Meanwhile, data protection regulations may need to evolve to address the unique challenges posed by LLMs. This could involve clarifying the requirements for data consent and erasure in the context of LLMs, setting clearer guidelines on data minimization for machine learning models, and addressing the complexities of ensuring compliance across different jurisdictions.


As we continue to explore the potential of LLMs, it is essential that we do so with a keen awareness of the data protection implications. This will ensure that the development and application of these powerful models proceed in a manner that respects individual privacy rights and complies with data protection laws.


 

Interesting fact: The fines for violating GDPR and CCPA can be significant. On the GDPR side, Amazon was fined €746 million ($781 million) in 2021 by Luxembourg’s National Commission for Data Protection (CNPD) for not obtaining consent from its users before storing advertisement cookies, the largest fine to date under GDPR​​. Other significant fines include Instagram being fined €405 million ($427 million) for violating children's privacy, Facebook fined €265 million ($275 million) after its personal data was found on an online hacking forum, Google fined €90 million ($99 million) for noncompliant cookie consent mechanisms, and Clearview AI, a facial recognition firm, fined €20 million ($20.5 million) for breaches of EU law​​. On the CCPA side, Facebook received the highest fine of $5 billion in 2019 from the U.S. Federal Trade Commission (FTC) for the Cambridge Analytica scandal and other privacy violations, although it ended up settling for $725 million​​. Other significant fines include Didi Global, a Chinese ride-hailing company, fined $1.2 billion for a series of violations related to data security and personal information protection, and Epic Games, the creator of Fortnite, fined $520 million by the FTC for violating the Children’s Online Privacy Protection Act (COPPA) and for deceptive interfaces​. These hefty fines underline the financial risk companies take when they don't comply with data privacy regulations.

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