AI World: Designing with Privacy in Mind

Artificial intelligence has the power to transform everything from our daily routines to groundbreaking medical advancements. However, to truly tap into AI's potential, we must approach its development with responsibility at the forefront.

This is why the discussion around generative AI and privacy is so crucial. We're eager to contribute to this conversation with insights from the cutting edge of innovation and our deep involvement with regulators and other experts.

In our new policy working paper titled "Generative AI and Privacy," we advocate for AI products to include built-in protections that prioritize user safety and privacy right from the get-go. We also suggest policy strategies that tackle privacy issues while still allowing AI to flourish and benefit society.

Privacy-by-design in AI

AI holds the promise of great benefits for individuals and society, yet it can also amplify existing challenges and introduce new ones, as our research and that of others has shown.

The same goes for privacy. It's essential to incorporate protections that ensure transparency and control, and mitigate risks like the unintended disclosure of personal information.

This requires a solid framework from the development stage through to deployment, rooted in time-tested principles. Any organization developing AI tools should have a clear privacy strategy.

Our approach is shaped by long-standing data protection practices, our Privacy & Security Principles, Responsible AI practices, and our AI Principles. This means we put in place robust privacy safeguards and data minimization techniques, offer transparency about our data practices, and provide controls that allow users to make informed decisions and manage their information.

Focus on AI applications to effectively reduce risks

As we apply established privacy principles to generative AI, there are important questions to consider.

For instance, how do we practice data minimization when training models on vast amounts of data? What are the best ways to offer meaningful transparency for complex models that address individual concerns? And how can we create age-appropriate experiences that benefit teens in an AI-driven world?

Our paper provides some initial thoughts on these topics, focusing on two key phases of model development:

• Training and development
• User-facing applications

During training and development, personal data like names or biographical details forms a small but crucial part of the training data. Models use this data to understand how language captures abstract concepts about human relationships and the world around us.

These models aren't "databases" nor are they meant to identify individuals. In fact, including personal data can help reduce bias — for example, by better understanding names from various cultures — and improve model accuracy and performance.

At the application level, the risk of privacy harms like data leakage increases, but so does the opportunity to implement more effective safeguards. Features like output filters and auto-delete become vital here.

Prioritizing these safeguards at the application level is not only practical but, we believe, the most effective way forward.

Achieving privacy through innovation

While much of today's AI privacy dialogue focuses on risk mitigation — and rightly so, given the importance of building trust in AI — generative AI also has the potential to enhance user privacy. We should seize these opportunities as well.

Generative AI is already helping organizations analyze privacy feedback from large user bases and spot compliance issues. It's paving the way for new cyber defense strategies. Privacy-enhancing technologies such as synthetic data and differential privacy are showing us how to provide greater societal benefits without compromising personal information. Public policies and industry standards should encourage — and not inadvertently hinder — these positive developments.

The need to work together

Privacy laws are designed to be adaptable, proportionate, and technology-neutral — qualities that have made them robust and enduring over time.

The same principles apply in the era of AI, as we strive to balance strong privacy protections with other fundamental rights and social objectives.

The road ahead will require cooperation across the privacy community, and Google is dedicated to collaborating with others to ensure that generative AI benefits society responsibly.

You can read our Policy Working Paper on Generative AI and Privacy [here](link to paper).