Will AI share my inputs with other users?

Introduction

Anytime we input information into software or an app, it’s crucial to consider where our sensitive data flows. Unless you’re a software engineer, tracing exactly how computer systems communicate with one another can be challenging. However, there are still some common-sense steps we can take to understand information security.

When it comes to generative AI, including public AI platforms, the same cybersecurity rules apply. We need strong passwords, trust in reputable software companies, and vigilance against attackers. Understanding how these systems work, including their AI aspect, can help you use them safely and responsibly.

Key Concepts

1. Data Flow and Cybersecurity Awareness: Understanding where and how to store data, how your input data is transmitted, especially across third-party services and processed when you input it into software or apps, emphasizing the importance of maintaining strong passwords, enabling multi-factor authentication (MFA), trusting reputable software companies, and staying vigilant against cyber threats.
2. AI Training vs. Usage: Differentiating between the process of creating AI algorithms and training AI applications, which involves feeding large amounts of data, including anonymized data, into the system to improve its performance, and using a generative AI service, a pre-trained AI, where the AI applies learned patterns to new inputs without further data collection.
3. Terms of Service Differences: Understanding the different terms of service between free and paid AI software and what they permit software companies to do with your data if you're on a free plan versus paid.

Where does my data go?

Initially, computers stored information locally unless exported, but as we moved online—through email and migrating to the cloud—it became essential to ensure the entire chain of systems that handle sensitive data is secure.

The modern internet is a complex network of digital systems. Often, when interacting with a single service provider, a complex network of different internal AI tools performs an array of functions to deliver what you see on your screen. Reputable software companies strive to ensure that data theft is avoided, and the data flowing in and out of their systems does so without any leaks.

To secure their systems, companies implement measures such as encryption, regular security audits, and MFA. Encryption ensures that even if surveillance data is intercepted, it cannot be read without the proper decryption key. Security audits identify and fix vulnerabilities before they can be exploited by attackers. MFA adds an extra layer of security by requiring users to provide two or more verification factors to gain access.

On the user side, it’s crucial to use strong, unique passwords for different services, enable MFA when available, and stay informed about security best practices.

Will my inputs be used to train AI?

When talking with someone, we know much of what we say will be remembered, at least in the short term. Similarly, when interacting with an AI chatbot capable of mimicking human conversation and thought, this system might also store what we tell it, contributing to data use. Some AI systems have started to implement memory systems for a more seamless experience.

AI training data

Modern large language models (LLMs), a type of AI capable of understanding and producing sophisticated text dialogues and other forms of digital content, are first created by assembling a massive set of training data. For instance, creating an AI model capable of interacting in English involves analyzing billions of samples of English writing. During AI development, researchers guide the model to recognize patterns in the text and the probability of their co-occurrence.

AI inputs

Even though a significant amount of data is used in the training phase, it's important to understand that once training is complete, this type of AI model does not reference its training data when producing outputs. As unlikely as it may seem, LLMs don't need any stored information to produce their remarkable outputs. Instead, using the patterns and probabilities learned during training, they generate text and other content by mimicking those patterns and predicting the next word or pixel in a series.

Some popular AI chatbots have added a memory feature that selectively stores input data about user interactions for a more seamless experience. For example, if a user asks for help writing a birthday message for a relative, the system may store details about the occasion and use this information the next time the user mentions the same relative. This memory does not alter the underlying AI model and is specific to the user.

Why are some AI models seemingly plagiarizing other people’s work?

It is true that users have been able to get mainstream AI language models to produce text and imagery resembling protected intellectual property. This is often because the work was strongly represented in the AI model’s training data. The more a given piece of content appears, the more influence it will have on model training.

This phenomenon can be understood as an artifact of how often correct examples of patterns appear in the training data. For any digital content widely available on the internet, AI systems can more easily mimic these assets because they have been trained on numerous examples. Many individuals and companies have filed lawsuits against leading AI firms, alleging infringement, opening a new field of legal analysis in AI technologies.

How can I make sure my data isn’t being used to train AI?

For decades, a dominant business model online has been the exchange of free services for user data. From search engines to social media, companies have provided their applications for people to use, free of charge, in exchange for being able to utilize their data.

This business model is also prevalent among generative AI companies. It’s crucial to be aware of the terms of service indicating the AI company's rights to use user activity for AI model tuning and product improvement. This scenario could mean that data a user inputs into the system might be used to update an AI model. While this does not necessarily mean the data will be shared with other users, it's important to be mindful of surveillance data use and privacy laws involved.

In all cases, it’s wise to analyze the terms of service and gain a strong understanding of privacy rights. If your work requires extra precautions, explore company-approved AI tools and closed, more secure environments being developed for greater security.

Conclusion

Understanding the flow of your data in the context of generative AI is essential for ensuring your information remains secure. Recognizing the importance of strong passwords, trusted software providers, and vigilance against attackers can better protect your data. Additionally, differentiating between AI training and usage phases helps clarify how your inputs are utilized and the potential risks involved. While AI models do not reference their training data for generating future AI outputs, some systems may store user-specific information to enhance AI interactions, emphasizing the need to be aware of such features.

Moreover, the prevalent business model of exchanging free services for user data necessitates a close examination of terms of service agreements. When using free AI services, it is crucial to understand that your data might be used for AI model tuning and product improvement. This awareness allows you to make informed decisions about the tools you use and the data you share. For those requiring heightened security, exploring closed-environment AI solutions may be beneficial. By following these guidelines, you can navigate the complexities of generative AI responsibly and securely.