Today, Facebook launched a significant initiative to address privacy concerns in AI research by partnering with online learning platform Udacity and privacy-focused AI organization OpenMined to create a course – Secure and Private AI.
The Privacy Challenge
As AI systems become more integrated into our daily lives, the question of how to utilize valuable data while protecting individual privacy has become increasingly urgent. This is particularly critical in fields like healthcare, where data-driven insights can save lives but must be balanced with strict privacy requirements.
“Without data scientists who know how to properly preserve privacy, private data is either left unused — a critical loss in fields such as health care — or is put at risk through data science techniques which lack the proper privacy protections,” explained Andrew Trask, Executive Director of OpenMined.
Facebook’s Educational Initiative
Acknowledging its own troubled history with privacy, Facebook took a proactive step by funding scholarships for 5,000 people to learn privacy-preserving AI techniques through Udacity. The initiative, announced at Facebook’s F8 developer conference, aimed to train a new generation of AI researchers in cutting-edge privacy technologies.
“I know that we don’t exactly have the strongest reputation on privacy right now,” Facebook CEO Mark Zuckerberg candidly admitted at the event, while introducing the company’s new mantra: “The future is private.”
The Secure and Private AI Challenge
The program, officially named the “Secure and Private AI Challenge Scholarship from Facebook,” offers a comprehensive education in three key privacy technologies:
- Federated Learning – A technique that allows AI models to be trained across multiple devices without centralizing sensitive data
- Differential Privacy – A technique to protect individual data while still extracting useful patterns
- Encrypted Computation – Methods that enable computation on encrypted data without revealing the underlying information
Upon completion of the initial course, 300 top-performing students would receive full scholarships to Udacity’s Deep Learning or Computer Vision Nanodegree programs, creating a pipeline of privacy-focused AI talent.
OpenMined’s Key Contribution
At the heart of this educational initiative was OpenMined’s PySyft technology. This open-source tool extends PyTorch (Facebook’s deep learning framework) with cryptographic and distributed technologies necessary to securely train AI models on private data.
Andrew Trask, who helped design the course curriculum, emphasized that the “most urgent barrier to the world becoming privacy-preserving is the lack of talented data scientists who know how to use privacy-preserving tools.“
The Path Forward
This collaboration between Facebook, Udacity, and OpenMined represented an important step toward reconciling the power of AI with essential privacy protections. By educating thousands of AI practitioners in privacy-preserving techniques, the initiative aims to help lay the groundwork for more responsible AI development.
As we continue to navigate the complex relationship between technological advancement and privacy protection, initiatives like this demonstrate how industry, education, and open-source communities can work together to address critical challenges at the intersection of AI and privacy.
