Good afternoon. I'd like to begin by thanking the chair and members of the committee for the invitation to speak today.
I'm Nestor Maslej, and currently I serve as a research associate for the Stanford Institute for Human-Centered AI. I am also a co-author and the lead researcher for the AI Index. Although my testimony today makes use of data from the AI Index, I am speaking as a private citizen, and my views are not representative of those of the Stanford Institute for Human-Centered AI.
The AI Index is an annual report, currently in its fifth edition, that aims to track, distill and visualize key trends in artificial intelligence. Our goal at the index is to be the best and most authoritative single source of information on trends in AI. The index aims to give policy-makers like you not only a deeper understanding of AI but also, crucially, an understanding that is grounded in empirical data.
It is this latter aim especially that informs my testimony today. I am here to answer the following question: What does data tell us about facial recognition technology? I will answer this question by tackling two sub-questions. First I will comment on capability. As of today, what can FRT do? Second I will examine usage. Who uses FRT—public and private actors—and how?
In terms of capability, there has been tremendous progress in the performance of facial recognition algorithms in the last five years. The index looked at data from the National Institute of Standards in Technology's face recognition vendor test, which comes from the U.S. Department of Commerce and measures how well FRT performs on a variety of homeland security and law enforcement tasks, such as facial recognition across photojournalism images, identification of child trafficking victims, deduplication of passports and cross-verification of visa images.
In 2017, some of the top-performing facial recognition algorithms had error rates anywhere from roughly 20% to 50% on certain FRVT datasets. As of 2021, none has posted an error rate greater than 3%, with the top-performing models registering an error rate of 0.1%, meaning that for every one thousand faces, these models correctly identify 999.
The index also shows that the performance of FRT deteriorates on masked faces but not by an overly significant degree. More specifically, performance is five to 16 percentage points worse depending on the FRT algorithm and dataset.
In terms of usage, FRTs are becoming increasingly deployed in both public and private settings. In 2021, 18 of 24 U.S. government agencies used these technologies: 16 departments for digital access or cybersecurity, six for creating leads in criminal investigations, and five for physical security. Moreover, 10 departments noted that they hoped to broaden its use. These figures are admittedly U.S.-centric, but they paint a picture of how widely governments use these tools and towards what end.
Since 2017, there has also been a total of $7.5 billion U.S. invested globally in funding start-ups dedicated to facial recognition. However, only $1.6 million of that investment has gone towards Canadian FRT start-ups. In the same time period, the amount invested in FRT technologies has increased 105%, which suggests that business interest in FRT is also growing. Our estimates also show that FRT is the 12th-most funded area out of 25 AI focus areas.
Lastly, a McKinsey survey of leading business executives, which we include in the index, shows that across all surveyed industries, only 11% of businesses had embedded facial recognition technology in their standard business processes, which trailed robotic process automation at 26% and natural speech understanding at 14% as the most embedded technologies.
In conclusion, I've presented some of the AI Index's key findings on the current capabilities and usage of FRT. It is my hope that the data I have shared usefully informs the committee's deliberation on the future regulation of facial recognition technologies in Canada. I'd be more than happy to answer any questions on the data I've presented and the implications that it may have.
Thank you.