The Future of Surveillance

The Future of Surveillance


I am going to be making the case that surveillance
is an area that effective altruists don’t tend to think that much about, but it’s potentially
an area that we should be thinking a lot more about, especially if we care about the long-run
future. So the way I’m going to make this case is
that first, I’m going to describe two ways in which the future of surveillance can be
quite bad. Then I’m going to describe a more positive
future, and argue that this isn’t something that appears very frequently in discussions
of surveillance, but something that plausibly more people in effective altruism ought to
be thinking about. Okay. So, first, here are a couple of outcomes we
don’t want for the future of surveillance. On the left, we have a depiction of what seems
to be perhaps a bit of a violation of privacy or not as much accountability as you’d want
for your system of surveillance. On the right we seem to have, perhaps some
sort of oversight. It seems like a significant security issue
is unfolding that perhaps we wanted a little bit more intense surveillance to prevent. Um, to sort of describe the first scenario
in a less of a caricature, the sort of concern here is that currently, governments are collecting
a very large amount of data about individuals all over the world. This seems to be increasing over time. Everything we do online mostly ends up collected,
people walking around with things in their pockets whic, which have microphones and cameras
and GPS locators. Surveillance cameras are becoming more prevalent
and just seems like in general, we should expect the amount of data collected on individuals
to keep going up over time. Probably more importantly, the ability to
use this data is also going up as well. Partly this is a better a matter of better
analysis, better data mining to identify individuals from mass collected data, things of that nature. Partly it’s a matter of being able to more
efficiently use the information which is gleaned from data mining. Just one quick example, which is fairly benign
but somewhat suggestive: in recent years it’s become somewhat common in some provinces in
China, to used facial recognition cameras to do things like automatically recognize
people who are jaywalking and automatically fine them. A bit of a slightly longer-term thing is the
idea of a social credit score. This is the idea of using large amounts of
data collected about people, including, for example, their social media postings or the
crimes they commit, and using this to assign people a score which will affect their employment
prospects, their ability to travel or get into certain schools. And although none of this stuff is yet, I
would say very significant, there’s some suggestion that in the long-run future, or if you let
this go for just a few more decades, we may see much more strong versions of social incentive
shaping and much more invasive forms of surveillance. In the long run you might be more concerned
about countries being more authoritarian or just political institutions we care about
working less well. The other category of risk that you might
be concerned about is the sort of ineffectual surveillance scenario. The sort of argument for this concern is that,
in the future, it may be the case that methods of causing large amounts of harm become a
lot cheaper or easier to use. So at the moment if, let’s say you’re an individual
with sort of unusual motives or bad motives, and you want to hurt a lot of people, it’s
not that easy to do that, to hurt more than a few hundred people. But this is to some extent, probably a matter
of what technology is available. Some people have suggested that, for instance,
synthetic biology, given perhaps a few decades, may make it easy for a relatively small groups
of people to design pathogens that can harm very large numbers. Other technologies which are sometimes discussed
with this sort of narrative are cheap drone swarms, in the sort of longer-term future
nano-weapons or potentially especially disruptive cyber weapons. It’s not necessarily clear that any of these
individual technologies is extremely likely to sort of have this property of making it
very cheap to cause large amounts of harm. But we can sort of draw an analogy to sort
of suggest what the significance of these technologies would be. So suppose that it turned out to be the case
that nuclear weapons were much cheaper to make than they in fact are, say that rather
than requiring massive state programs and years and years of work, that anyone could
fairly easily construct nuclear weapons from household materials. It seems like in a world of that sort, the
odds that they wouldn’t be used would be very, very low, and you’d likely need some very
pervasive form of surveillance to actually catch people who were planning to cause this
large amount of harm. So we don’t know that any future technology
will have these properties, but it seems not entirely impossible that one might. And if that’s the case then we’ll want, probably,
much more effective forms of surveillance. So something which is typically a part of
discussions of surveillance is this sort of trade off narrative. So on the one hand, there’s this idea that
the more you protect people’s privacy, the less you allow governments to actually protect
people’s security. And on the other hand, there’s this idea that,
the more you make government accountable, sort of let people know what they’re up to,
the less effectively governments will be able to operate. So sort of to explain or justify the privacy/security
trade off, let’s take the case of someone who’s carrying a bag that may or may not have
a bomb in it, and consider a police officer who’d like to know if it has a bomb, who doesn’t
have any tools available to them. It seems like their two options are to, first
of all, they can potentially open up the bag and look at what’s inside, see if there’s
a bomb. But in the process they’ll figure out everything
else that’s in the bag, and some of this might be quite personally revealing. On the other hand, they can choose not to
open the bag, and therefore not violate the person’s privacy, but in choosing not to open
the bag, they also don’t learn whether or not there’s a bomb inside. The accountability/security trade off, the
idea here is that, let’s say take the case of a protocol which is used to select people
for search or a special scrutiny. We may want to know that the protocol is actually
being followed, that an individual isn’t deviating from it. We may also want to know what exactly the
details of the protocol are. Is it something which is discriminatory? Is it something which is fair? Does it have a sufficiently high accuracy
rate? A case which is often made by governments
to sort of keep their protocols secret is to argue that, if you make the details of
the protocol public, then people can figure out how to get around it and it becomes much
less effective. And so this trade off narrative seems to suggest
that steering away from one risk means steering towards the other. Even if you have the mindset that only one
of these two risks is actually credible or of significant importance other than the fact
that other people care about the other risks, it means that there will be political constraints
on pursuing solutions to one risk or the other. As a sort of an extreme caricature, let’s
say that you’re someone who doesn’t care about privacy at all, you think that the risk from
authoritarianism isn’t in any way important, and you think would be really great if the
government put cameras in every single person’s home and watched them all the time. The fact that other people are definitely
not cool with that, and care about privacy means that that would just be a nonstarter
as a solution. So in general, it seems like the more severe
the trade off is between these values, the more concerned we should be about either risk
or both risks together. This all seems to suggest that a useful thing
to do would be to look for opportunities to reduce these two trade offs. This means looking for ways to make surveillance
more accountable and privacy preserving. While this sounds a bit idealistic, we can
get some intuition that this is possible by looking at different forms of surveillance
which are applied today, and that definitely vary quite significantly in how much they
protect people’s privacy. So if we were trying to get into the case
of a bag that may or may not have a bomb in it, suppose that instead of just opening the
bag, a police officer has access to a bomb sniffing dog. In this case, they can have the dog come up
to a bag, sniff it. If it barks, they open the bag and search. If it doesn’t bark, they don’t. In the idealized case with dog that has a
perfect accuracy rate, they only learn exactly what’s relevant for security. Does a person have a bomb? But they don’t violate people’s privacy in
any other way. And the more accurate it is, the less it violates
people’s privacy. At the same time, this is also a fairly accountable
form of surveillance. If you have your bag on you, you can tell
that a dog was used rather than just a person rifling through it. You can also tell whether the dog barked. It’s difficult to lie that a dog has barked
when it hasn’t because you can hear it. Yeah. So that’s a sort of a specific case. A more sort of abstract case for optimism
is that in the future, it’s likely the case that surveillance and law enforcement becomes
more heavily automated. While this has a number of scary components
to it, there’s also some reasons to think that this trend may actually make it easier
to ensure privacy and accountability. So here’s some basic advantages of automation. So first of all, if you automate an analysis
task that would ordinarily be performed by human, then you can use software as a screen
between data and the humans who see it. So the analogy is to a sniffing dog. Again, if you have some piece of software
which looks at data and make some initial judgment of whether to search further, then
potentially human doesn’t need to look at data that they would otherwise look at. You can also potentially you redact sensitive
information automatically so that no human ever needs to see it. So one concrete example would be automatic
face blurring of faces that appear in police body camera footage. In certain regards, algorithms are also more
predictable and less opaque than humans. To some extent, AI is often a black box, but
it’s less of a black box than the human brain is. You can’t really look at a human police officer’s
brain to sort of see what’s going on there. But you can often look at the source code
for software. It’s also often easier to associate things
are done with software with reliable audit logs, as opposed to let’s say, trusting human
analysts to record what they’re up to. Software’s also less likely to engage in certain
abuses that a human might. So one slightly disturbing example of this
is, there’s this concept, at least in the past, hopefully not in the present within
the NSA of LoveInt, where this is short for love intelligence. The idea is looking at information on a significant
other or an ex, and this was apparently common enough that they had jargon for it within
the NSA. Seems like something weird has happened if
software that you’ve designed is doing that. At the same time, if you’re using software
in place of humans, it also potentially becomes easier and more efficient to automate a single
piece of software, as opposed to auditing lots of different humans who might be replicating
this behavior. So if you’re using a single piece of software
in lots of different cases, then potentially you just look at the single piece as opposed
to lots of different human analysts and officers and officials, who might be deviating from
protocols. It’s also easier to associate a piece of software
which is applied in lots of different cases with summary statistics, for example, in accuracy
rate, compared to using statistics for humans. So I think this is actually like a large issue
currently with basically surveillance in law enforcement at the moment, is that it seems
like intuitively if you’re establishing probable cause or reasonable suspicion, it’s sort of
like a probability threshold for that. Exactly how accurate is, for example, an officer’s
judgment that someone meets these thresholds? What portion of the time are they right? For an individual officer, this isn’t extremely
feasible to collect these statistics, but, for example, using a facial recognition system,
you actually have fairly good data on exactly how accurate it is. You can actually have a fairly informed discussion
about what sort of false positive rate is too high. That’s a bit hard to have for humans. A couple of less obvious advantages are that
increasing automation can actually decrease the need for data collection, and that increasing
automation can decrease the disruptiveness of engaging in auditing. So the idea for surveillance without data
collection, the basic idea is certain cryptographic technologies make it possible to analyze data
or extract certain pieces of information from it without collecting the data in unencrypted
form. Probably the most notable one is a technology
called secure multiparty computation, which is extremely general. And recently just in the past decade or so,
became much more practical to use. A couple of examples of how this technology
can be used: so the first is the idea of a set intersection search. So this comes up fairly commonly in law enforcement
contexts, where you want to identify someone that’s suspicious on the basis of the fact
that they show up in a few different databases. So one concrete example is, say someone robs
a few different banks. Police know it’s the same person, but they
don’t know who it was. They might want to search the cell records
for the cell towers that were near all three of the banks to see if anyone made calls near
all three of them. The way you would traditionally do this, in
a way that actually historically has been done, is to collect all of the records from
these three cell towers, get tens of thousands of people’s records, and then comb through
them and see if any name pops up three times. But it’s actually possible to do this without
mass collecting records in this way. So there’s a paper in 2014, that shows how
to conduct a search of this sort. Where you get out a list of names that appear
in all three of these databases, but you don’t get any other information besides just those
names. So rather than collecting tens of thousands
of people’s information, you collect maybe one or two. Another example is fraud detection. So for the case of value added fraud detection,
one way you sometimes do this is by finding discrepancies between different companies’
private financial records. One, let’s say, reports a purchase, that doesn’t
match another country’s report of a sale. There’s a paper in 2015 that describes a protocol
that I believe has actually now been used by the Estonia government to find cases of
tax fraud of this sort, without collecting companies’ private financial records. They get this output of here are the discrepancies,
but they don’t actually get any unencrypted records, sort of taken in, so they can’t learn
anything else other than just who is showing discrepancies. Another example, which I’m also not going
to get into the technical details of, is this idea, traceable to a paper by Joshua Kroll
in 2016, to use a cryptographic technology called zero knowledge proofs to produce accountable
algorithms. The basic upshot is that he shows that it’s
possible in many cases to prove to the public that a protocol that’s received their approval
is still being applied. That people aren’t straying from it. And also that the protocol has certain desirable
formal properties, for example, fairness properties, without actually making it public, what the
details of the protocol are. So that’s desirable, if the reason for not
making the details of a protocol public or that you can say, oh, if we made them public,
people could get around it, or potentially you’re a law enforcement agency and you can’t
make the details public because some private company has developed it for you and it’s
quite profitable. So this is a way of making things more accountable
while dodging those objections that making myself more accountable would make you less
effective. So in my fairly non-expert view, I see sort
of a few opportunities for things effective altruists could potentially add to the conversation
around surveillance. So one is that in my opinion, the conversation
is typically too focused on managing trade offs. For example, debating exactly how much security
you can get by trading away this amount of privacy, or sometimes denying that these trade
offs exist to any extent, which seems implausible. Rather, I think it’d be more useful to look
ahead to technical solutions for actually sort of reducing these trade offs. Another concern I have is that a lot of the
conversation concerns current programs, or programs which are just getting off the ground. I think it would also be productive to have
a conversation about what forms of surveillance we might want to be moving toward, let’s say
over a 10 or 20 year period as new risks emerge, and also as new technologies make different
forms of surveillance feasible. And then the last one is that I think discussions
of surveillance are often sort of reliant on assumptions about technology which aren’t
actually true or are going to become less true in the future. So a classic one is just that analyzing data
actually requires collecting data, which isn’t actually technically true. One last comment is that this presentation
has been all about, sort of mass surveillance, but a lot of what I’ve said also applies to
the case of agreement verification in an international relations context. So, the idea here is that frequently you want
to verify compliance with an agreement, let’s say an arms agreement, but often the process
of monitoring the country or verifying compliance, involves collecting lots of information which
is sort of revealing in a way which is viewed as negative. So for example, it gives away to details of
weapons systems or allows countries access to private actors’ labs that might be indicative
of sort of valuable intellectual property. And if you can find ways to make sort of more
privacy preserving a forms of monitoring in the same way, you can make more privacy preserving
forms of surveillance. and this could potentially reduce the bottleneck on the ability to actually
reach international agreements. And this is also something that seems to intersect
a lot with existing effective altruist concerns around global catastrophic risks and governance
of emerging technologies. So just in closing, in the future, surveillance
might threaten the institutions that we care about, or it might fail to protect us for
new threats. It seems like there’s some sort of trade off
between addressing these risks, but these trade offs also don’t seem to be immutable. There is some hope that in the future, technological
progress can help reduce these for us. So therefore it seems like the project of
pursuing accountable. privacy-preserving surveillance, while not
something that many people are engaging in at the moment, might be something that more
effective altruists want to look into or signal boost in conversations around surveillance. I’m going to be giving office hours at 10:30
AM tomorrow, if anyone wants to talk more about that, and I’m also just generally free
to talk if anyone has any interest. Yeah. Thank you so much.

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