Certainly. That's an excellent question. It speaks to the fact that although regulation of facial recognition technologies certainly matters, there are also other AI use cases that might merit regulatory attention.
One of the takeaways from the data we have on facial recognition is that facial recognition is in the middle of the pack in terms of total private investment. It's more invested than things like drones or legal tech, but it's behind NLP in medical and health care, which suggests, as well, that there are other AI use cases that might merit further attention.
As the data from the McKinsey survey suggests, facial recognition is not as embedded in certain business technologies and processes as are other AI systems. Again, this doesn't necessarily mean that we shouldn't care about facial recognition regulation; it's an issue of utmost importance. It's just that we should also be cognizant of the other problems AI might pose, especially at a time when AI is becoming increasingly ubiquitous.
You alluded to a couple of the different examples that we cited in the report. I'll speak to a couple of different ones.
We talked about the fact that there are résumé-screening systems that have been shown to discriminate based on gender. We cite evidence from a newspaper article that a couple of years ago, Amazon developed a machine-learning résumé-screening system that was shown to systematically downgrade the applications of women.
Again, it would be ideal for a lot of these companies, especially the very big ones, if someone gave them 100 résumés and they could just give them to a machine that says automatically that these are the best three candidates and just hire these three people.
The reason Amazon trained a biased system was that the system was ultimately trained on data from résumés that were submitted to Amazon previously. Overwhelmingly, the résumés that tended to be submitted to Amazon were submitted by men. This reflects the fact that the tech industry is mostly dominated by men. Given that men were mostly traditionally hired, the AI system learned to penalize the term “women”. That meant that if you included a resume that, for example, said, “I was captain of the women's swim team,” the algorithm saw that historically very few women have been hired at Amazon, this person has “women” in their résumé, so let's downgrade this résumé. Amazon claimed that this tool was not actually ever deployed to make hiring decisions, but the point stands that this bias remains.
We also talk about bias in multimodal linguistic models. We talk, as well, about bias in medical image segmentation. I could go on at length about that, but I'll perhaps give you the opportunity to pose additional questions.