This entry is based on the Defcon talk “Life Inside a Skinner Box*: Confronting our Future of Automated Law Enforcement” by researchers Lisa Shay, Greg Conti and Woody Hartzog about downsides of automated surveillance and law enforcement.
Lisa Shay: Good afternoon. I’m Lisa Shay, I teach Electrical Engineering at West Point. I’m here with my colleague Greg Conti, who teaches Computer Science at West Point, and our friend Woody Hartzog, who is a lawyer. He’s an assistant professor at the Cumberland School of Law, which is part of Samford University. He’s also an affiliate scholar at the Stanford Center for Internet and Society, and he’s worked at the Electronic Privacy Information Center as well.
So, we are going to talk about confronting automated law enforcement, which is any use of automation, computer analysis within the law enforcement process. These are our own ideas, our own thoughts, not those of our employers.
The whole idea is: we like living in a free society, and we don’t want to live in a police state.
You think about: “It’s always good to obey the law, that’s part of our value system, that’s part of what makes society work.” But think about what happens if everyone obeys every law rigidly all the time. This is a video that many of you have seen where a group of students at Georgia Tech tried an experiment: they drove around the beltway around Atlanta at exactly the speed limit, and they got a bunch of friends together and drove right across the entire highway at exactly the speed limit (see right-hand image). And you can see they’ve got awful traffic jam that’s building up behind them. And the people behind them, you can imagine, were just thrilled to be going exactly the speed limit. It actually could have been a very dangerous experiment: there were people driving on the side of the road to try and get around this.
So, as I said, automated law enforcement is any computer-based system that is going to use input from unattended sensors that we have all around us to algorithmically determine whether or not a law has been broken and then to take some kind of action. So, really, what we want you to take away from this talk is three things:
1. The networked technologies exist right now.
2. If we aren’t careful in paying attention to how these systems are emplaced, really disastrous consequences could ensue. We could end up living in that police state we showed earlier.
3. You all in this audience are in a unique position to help prevent this. You have the technical knowledge, you have the networks, you’ve got the skills and abilities to see what’s going on and to ask the right questions of people who are trying to implement these systems. So, over to you, Greg.
Greg Conti: So, what leads us to this conclusion? Well, we argue the precursors are in place for this now and it’s a natural extension to what we can see what’s going on now and look into the very near future to see where it’s all heading, and hence to the motivation for our talk to try and generate some interest in deflecting the trajectory of this.
Just imagine the sensors in your home, the sensors on your body, the sensors in your body, the sensors in your car, the sensors in your community; they are proliferating at a massive rate, and that creates data and data flows at enormous amount increasing views on our lives from every angle that can be sampled by a sensor.
And we’re seeing increased diversity and sensitivity of sensors. This is an example of someone who had a medical test that injected a radioactive compound into the body for a medical reason: driving home sets off a radioactivity sensor in a police car and gets pulled over (see left-hand image). And this is actually true. But that’s the type of things we’re seeing – the increased diversity of sensors.
We’re seeing sensors becoming mandatory, as in the United States, this trend toward mandatory black boxes in cars to track. We see increased mobility of sensors (see right-hand image). In the long sight we see the transfer of military technology such as drones into law enforcement roles.
Probably one of the largest areas of concern is the mobile device that we carry on our pocket that discloses our location (see left-hand image). It’s replete with sensors and high-speed network connectivity.
So what I’m trying to paint here is a portrait of where we are now; all these precursors are in place. If you examine them individually – ok, maybe you don’t see so much, but if you put them all together, something larger and more concerning emerges.
The idea of connected cars in OnStar: sometimes you can’t get the car, and particularly in a rental, without it – discloses your location on this full-time connection (see right-hand image). If they can start your car remotely, presumably they can turn it off remotely as well.
We’re losing control of our technology, and there is a great quote here from Cory Doctorow (left-hand image), but the idea is more and more we’re having closed-source technologies where it’s illegal to lift the lid and look inside the firmware or the software. So, general-purpose computing is under attack, and Cory has a great talk on that; I highly recommend it.
So, we got these data flows. But once you have this data, what’s the key component to identifying the people that are potentially the subjects of this law enforcement system? Obviously, there’re current advances in facial recognition systems.
In the long sight mandatory biometric databases at the nation state level being constructed, such as in India, where there’s over a billion people being enrolled in such a system.
I think we’ll all admit that facial recognition isn’t there yet, it has its flaws, but hybrid systems are emerging to allow identification of individuals. And this is from identifyrioters.com (see left-hand image). It’s a great time to be alive when there is a website called identifyrioters.com, but this is from the Vancouver riots, where they are trying to solicit crowdsourced identification of people who were allegedly involved in the riots.
There are even business models, like, say, convenience store camera monitoring is being crowdsourced, and then they give small rewards to the citizens. So, what can’t be automated, can be combined in a human-machine hybrid system.
* Skinner Box – is a laboratory apparatus used in the experimental analysis of behavior to study animal behavior. (Wikipedia)