At the Rise of AI Summit 2020, Thomas Strottner, Head of Business Development at brighter AI Technologies, filling in for the company's CEO, explains how his company reconciles data privacy with AI development on camera data. He describes cameras as the richest sensor for AI use cases such as Amazon Go stores, smart city people counting and self-driving cars, and notes facial recognition systems can now track up to 20,000 key points on a face, which brings both useful applications and risks of abuse, citing facial recognition used to track Black Lives Matter protesters and for social scoring in authoritarian regimes. He surveys tightening regulation, including GDPR and California's new Consumer Privacy Act, and describes a perceived "AI privacy dilemma" between accurate models and privacy protection, which he argues is solvable through approaches like federated learning, privacy by default and privacy by design. He explains that pixelating faces and license plates degrades AI training data and introduces bias, and presents brighter AI's alternative: generating synthetic faces and license plates that preserve realism while, in tests against Microsoft Azure's facial recognition, reducing identity match to about 16%.
Key takeaways
- Strottner says modern facial recognition can detect up to 20,000 key points on a face, enabling both useful applications and abuses like protester tracking and social scoring.
- He presents federated learning, privacy by default and privacy by design as concrete ways to ease the perceived trade-off between accurate AI models and data privacy.
- He argues pixelating faces and license plates for anonymization introduces artifacts and bias into AI training data, such as models expecting pixelated plates in real driving.
- Brighter AI generates synthetic faces and license plates that, in tests against Microsoft Azure's facial recognition, matched the original person only about 16% of the time.
- He frames California's GDPR-like Consumer Privacy Act and Europe's regulatory head start as a business opportunity for European privacy-centric AI solutions.
Detailed notes
Cameras as the richest AI sensor
Strottner introduces brighter AI, which recently won the Berlin Deep Tech Award, and frames camera data as the richest sensor for AI use cases: Amazon Go's automated checkout stores, smart-city applications like people counting, and self-driving cars, where he notes Tesla emphasizes cameras as the most important sensor. He argues AI development needs both data quantity and data quality, and that privacy is often seen as standing in the way of both.
Facial recognition's promise and abuse
He points to facial recognition as one of AI's notable achievements, such as unlocking a phone with a face, enabled by modern neural networks that can now detect up to 20,000 key points on a face. He balances this against real risks of abuse, citing facial recognition used to track individuals at Black Lives Matter protests and its use by authoritarian regimes for social scoring, alongside growing individual and societal pushback on privacy.
A tightening regulatory map
Strottner presents a map of global data protection regulation strength, noting the US, Canada, European countries under GDPR, and even China are marked as having heavy regulation, which restricts how companies such as automakers can collect camera data in public. He highlights California's new Consumer Privacy Act, closely modeled on GDPR, as an opportunity: because California is one of the largest economies and home to major tech companies, he sees a chance to export European privacy-centric solutions, noting San Francisco has banned facial recognition technology. He references a study brighter AI conducted with the German AI association on video data regulation across countries, stressing that internationally operating companies must track diverging national rules.
The AI privacy dilemma and how to address it
He describes a commonly perceived dilemma between powerful, accurate AI models built on clean data and privacy protection achieved by degrading data, such as blurring faces. He presents several approaches to ease this: decentralized processing such as federated learning, where models train across distributed devices without exchanging raw data, as used by Apple; privacy by default, which sets the most privacy-protective settings as standard, as GDPR requires and as seen in cookie banners; privacy by design, building protection into the technology itself, for example through encryption, pseudonymization or full anonymization; and altering the data itself through synthetic data or data augmentation, the area where brighter AI operates.
Synthetic data and brighter AI's synthetic face solution
For self-driving cars, he discusses fully synthetic, generated-from-scratch data and digital twins as ways to avoid privacy issues entirely, though he notes synthetic scenarios can never capture the full creativity of real-world edge cases, and that data augmentation, adding artifacts like fog to existing clean images, can help generate more corner cases from privacy-compliant data but can never fully reflect reality and can introduce bias. He argues the common alternative, pixelating faces and license plates, introduces artifacts, such as a segmentation model detecting a "phantom person" or a bird where pixelation appears, and can bias models, for example training a network to expect pixelated license plates that do not exist in real driving. Brighter AI instead generates synthetic faces and license plates that look natural. Tested against facial recognition systems including Microsoft Azure's, the synthetic version of the same image and scene produces only about a 16% match to the original person, meaning it effectively cannot be re-identified, while the resulting data can still be used to train AI models for clients in automotive, data labeling, retail and smart cities.
Q&A highlights
Asked why Silicon Valley companies do not adopt privacy-by-design themselves, Strottner says major players are already engaging with privacy, citing one large Silicon Valley company opening a data-privacy office in Germany, and argues Europe's first-mover position with GDPR gives it a reputation and expertise advantage. Asked whether synthetic faces work for research beyond simple detection, such as emotion recognition, he says brighter AI's synthetic faces preserve the original's emotion, age and gender attributes without introducing artifacts, though use-case-specific testing is still needed. Asked about the biggest unresolved challenges ahead, he says the scale of data needed for self-driving and retail development makes privacy-compliant data a significant, ongoing problem that brighter AI is positioned to address.
Quotes
We see cameras as the richest sensor for innovative AI-based use cases.
Thomas Strottner
Today these systems can detect up to 20,000 key points in your face in order to track an individual.
Thomas Strottner
This is like often the perceived dilemma, and the question: which pill do you have to swallow?
Thomas Strottner
Questions answered in this talk
How does brighter AI's synthetic face technology protect privacy in camera data?
According to Thomas Strottner at the Rise of AI Summit 2020, brighter AI generates synthetic faces and license plates that look natural rather than pixelated, and in tests against Microsoft Azure's facial recognition the synthetic version of an image matched the original person only about 16% of the time, effectively preventing re-identification.
Why is pixelating faces and license plates a problem for training AI systems?
According to Thomas Strottner at the Rise of AI Summit 2020, pixelation introduces artifacts, such as a segmentation model seeing a phantom person or object, and can bias models, for example training a self-driving car system to expect pixelated license plates that do not exist in real-world driving.
How do privacy regulations like GDPR and California's Consumer Privacy Act affect AI companies using camera data?
According to Thomas Strottner at the Rise of AI Summit 2020, GDPR-style regulation already restricts how companies can collect public camera data in Europe, and California's new Consumer Privacy Act, modeled closely on GDPR, is creating a similar market in the US, which he sees as an opportunity for European privacy-centric solutions.