Updated: Oct 30
When we choose to allow OpenAI's ChatGPT, or any large language model (LLM), to use our data, there are risks and benefits associated. I'm often asked, "What is safe to share in ChatGPT? How is my personal data being used? Why would I allow them to use my data?" To address these questions so that you can make informed data privacy choices, I did an analysis of OpenAI’s use of our data.
The data that ChatGPT uses to function and to improve its services includes text inputs (prompts), user preferences, past conversations or interactions, feedback and rating data, and metadata (timestamps, device type, or browser information). I crafted this data usage analysis from two documents: The ChatGPT Data Usage for Consumers FAQ and User Content Opt-Out Request Form (as posted online on October 1, 2023). In this blog, I specifically explore:
The risks users encounter when sharing their data
How our data is used in fine-tuning the ChatGPT models
Examples of when OpenAI uses our data to "comply with legal obligations"
What we lose when we don’t share our data
Actionable steps to protect ourselves
Risks of Sharing Personal Data
When you choose to share your personal data, especially with AI models like ChatGPT, it's important to be aware of the potential risks
involved. Here are some key risks to consider:
Privacy and Confidentiality: Sharing your data exposes your conversations, prompts, responses, and uploaded images to the service provider. Although measures are taken to secure the data, there is always a risk of unauthorized access or data breaches. Example: Suppose you share personal anecdotes or sensitive information during a ChatGPT session. Despite security measures, there’s a risk of unauthorized access or data breaches which could expose this personal information.
Data Usage and Storage: Your data may be stored on servers located in different jurisdictions, raising concerns about data sovereignty and varying data protection regulations. Example: If you are based in Europe and your data is stored on servers in the United States, the data protection laws of the U.S. will apply, which arguably does not offer the same level of protection as the GDPR in Europe.
Data Sharing with Third Parties: Service providers may share your user content with trusted third parties to facilitate service provision. While confidentiality obligations exist, there is still a possibility of unintended data sharing or unauthorized access. Example: The service provider might share your data with a third-party cloud service provider to manage server loads. In an undesired scenario, a security breach at the third-party could lead to unauthorized data access.
Human Access to Content: Authorized personnel and contractors may have access to your data for support, abuse investigations, or model fine-tuning purposes. Controls are in place, but the potential for human access introduces privacy risks. Example: If you reach out for support regarding an issue you’re facing with ChatGPT, a support representative might need to access your conversation history to understand and resolve the issue, thus accessing your data.
Lack of Control Over Data: Once shared, you have limited control over your data. Example: If you stop using ChatGPT, the service will continue to use your previously shared data to improve its models.
Digging into 3 Concerns
After assessing the above details, I had 3 specific concerns that I felt we all need to further understand: 1) What is included in “fine-tuning of models,” 2) examples of “complying with legal obligations,” and 3) what are the trade-offs of not sharing our data.
Fine-tuning is a process that utilizes user-submitted data to enhance the performance and capabilities of AI models. While the specifics are not fully disclosed, here are possible examples of what fine-tuning may involve:
Language and Grammar Improvements: User interactions can be used to refine language generation and improve grammar, resulting in more accurate and coherent responses. Example: If you point out that the phrase “more better” is incorrect, the system can learn from this interaction to correct its usage in the future, opting for “better” or “much better” instead.
Contextual Understanding: User conversations help the model better understand and maintain context, enabling more relevant and meaningful responses. Example: Suppose you are discussing a dog and you later mention “taking him for a walk.” The system can use past interactions to understand that “him” refers to the dog, ensuring the conversation flows naturally.
Common Use Cases: Fine-tuning may focus on improving the model's performance in specific domains or addressing frequently asked questions to enhance user experience. Example: If multiple customers ask how to reset their passwords, the system can learn to provide a more direct and efficient response to this common question, enhancing the customer experience.
User Experience Enhancements: Our data can be leveraged to optimize responsiveness, speed, and overall satisfaction, making the model more efficient and user-friendly. Example: Suppose we frequently express frustration with slow response times. By analyzing this feedback, the system might be fine-tuned to prioritize speedier responses.
Safety Measures: Fine-tuning can train the model to identify and avoid generating harmful or inappropriate content, contributing to a safer experience. Example: If we often report receiving inappropriate responses to certain queries, the system can be fine-tuned to recognize and avoid these inappropriate responses in future interactions.
Examples of Complying with Legal Obligations
To ensure legal compliance, service providers must adhere to various obligations. Here are potential examples of complying with legal requirements:
Law Enforcement Requests: Service providers may be obligated to provide user data to law enforcement agencies in response to valid legal requests, such as subpoenas or court orders. Example: If law enforcement suspects a user is involved in illegal activities and obtains a court order, the service provider will be required to share the user’s chat history with the authorities.
Investigating Abuse or Violations: Accessing and analyzing user data may be necessary to investigate and address violations of terms of service, code of conduct, or applicable laws. Example: If a user is reported for harassment or sending threatening messages through the platform, the service provider might need to access and analyze the user’s data to investigate and address the violation.
Intellectual Property Disputes: User data might need to be disclosed in cases involving intellectual property infringement claims or legal actions. Example: In a scenario where a user claims to have shared proprietary information via ChatGPT and accuses another party of stealing this information, the service provider might need to disclose chat history in a legal proceeding to resolve the dispute.
Compliance with Data Protection Laws: Service providers must adhere to data protection and privacy laws in the jurisdictions where they operate, ensuring user data is processed in accordance with applicable regulations. Example: A service provider might need to implement specific data handling and processing measures to comply with GDPR in Europe, ensuring that user data is handled in accordance with these regulations.
National Security or Public Safety: In certain circumstances, service providers may be required to share user data with government agencies or authorities for national security or public safety reasons. Example: Under the USA PATRIOT Act, law enforcement agencies can request access to personal data held by service providers as part of investigations into terrorism or other serious crimes. Suppose a user of ChatGPT is suspected of involvement in a terrorist network. Law enforcement, upon obtaining the necessary legal authorizations, could request the chat history and other data related to this user to aid in their investigation.
The Trade-Off: Opting Out of Data Usage for Model Improvement
While the option to opt out of data usage for model improvement provides enhanced privacy control, it's crucial to understand the trade-off involved. By choosing not to share your data for the purpose of training and fine-tuning AI models like ChatGPT, there are implications that can affect the quality and personalization of the AI's responses to your specific needs.
Reduced Personalization: AI models rely on user data to learn and adapt to individual preferences, conversation styles, and specific use cases. When you opt out, the models will have limited exposure to the intricacies of your interactions, resulting in responses that may be less personalized and tailored to your unique requirements. Example: If you frequently discuss digital marketing strategies in your interactions with ChatGPT, over time, it better understands and adapts to the terminology and context specific to digital marketing. However, if you opt out of data sharing, the model will not retain this contextual understanding in future interactions, leading to less tailored responses to your digital marketing inquiries.
Generalized Responses: Without access to your specific conversations and prompts, the models will lack the context and insights necessary to generate highly specific and targeted responses. As a result, the AI's replies may become more generalized, potentially overlooking the nuances of your particular use case. Example: Let’s say you’re working on a niche programming project and often ask ChatGPT for help with specific coding issues related to a lesser-known programming language. If you opt out of data sharing, ChatGPT will lose the context of your past interactions and your ongoing project. Consequently, when you ask for help on a complex issue, instead of providing a targeted solution, ChatGPT will offer more generalized programming advice that is less helpful or relevant to your particular problem.
Limited Domain Expertise: Fine-tuning allows AI models to specialize in specific domains by learning from user interactions in those areas. If you opt out, the models do not have access to the detailed knowledge and patterns within your domain of interest. Consequently, their ability to provide accurate and comprehensive responses in that specific domain is compromised. Example: Assume you are a legal professional who frequently seeks insights on case law through interactions with ChatGPT. Over time, with data sharing enabled, ChatGPT develops a nuanced understanding of legal terminology and the types of case law insights you seek. However, if you opt out of data sharing, the model loses its ability to provide precise legal insights, and its responses lack depth or relevance to your specific legal inquiries.
Opting out of your data being used for model improvement does not render the AI models entirely ineffective. They will still rely on their pre-existing knowledge base and the general patterns observed from other users' interactions. However, by not sharing your data, you are essentially excluding yourself from the continuous learning loop that helps the models improve and adapt over time to interactions with you.
The decision to opt out of data usage for model improvement should be made with a clear understanding of the potential consequences. Carefully review the privacy policies and terms of service provided by the AI service to gain insights into the specific trade-offs involved. By weighing the benefits of privacy against the advantages of personalized and accurate AI interactions, you can make an informed decision that aligns with your individual preferences and requirements.
If Caution is More Valuable than Personalized Results
Data Controls: Make use of the available data controls within the ChatGPT settings to manage your data preferences. Enable or disable features like chat history and data usage for model improvement based on your comfort level.
Opt-Out Requests: If you do not want your data to be used for model improvement, consider submitting an opt-out request as provided by OpenAI. This may limit the ability of the models to address your specific use case but can enhance privacy.
Exercise Caution in Sharing Sensitive Information: Be mindful of the information you share with ChatGPT. Avoid sharing personally identifiable information or sensitive data that could potentially be misused.
Regularly Clear Chat History: Take advantage of the option to clear specific chat conversations from your history to reduce the amount of stored data.
Stay Informed: Keep up with updates and changes to OpenAI's data usage policies. Regularly review the FAQs and documentation provided by OpenAI to stay informed about how your data is being used and any new privacy features or options.
By following these steps, you can take an active role in protecting your personal data and maintaining your privacy while using AI models like ChatGPT. But remember, you’re sacrificing personalized results from the models, and no system is entirely risk-free. Personally, the benefits of the personalized results outweigh my data privacy concerns so I choose to fully engage with ChatGPT and other LLMs.
Each of us must individually decide what our acceptable risk threshold is as we engage in this new world of generative AI. That's why I took the time to complete this analysis and share it with you. You need information in order to make an informed choice that allows you to both benefit from LLMs, particularly ChatGPT as the focus of this analysis, and also safeguard your personal data. Sharing data enhances the model's accuracy and elevates the quality of responses but comes with privacy and security concerns. Conversely, opting out of data sharing offers more privacy but at the cost of personalization and usefulness. By understanding OpenAI's data usage policies, exercising available data controls, and being prudent with the information shared, we can better navigate the trade-offs involved. It's my hope that this blog allows each of us to confidently set our risk threshold while we embrace the power of these tools.