Yesterday I argued that allowing police to record all communications that are flagged by some automated algorithm might be reasonable, if the algorithm is being used to recognize the voice of a person believed (for good reason) to be a criminal. My argument, in part, was that that kind of wiretapping would still be consistent with the principle of individualized suspicion, which says that we shouldn’t wiretap someone unless we have strong enough reason to suspect them, personally, of criminality.

Today, I want to argue that there are cases where even individualized suspicion isn’t necessary. I’ll do so by introducing yet another hypothetical.

Suppose we have reliable intelligence that al Qaeda operatives have been instructed to use a particular verbal handshake to identify each other. Operatives will prove they were members of al Qaeda by carrying out some predetermined dialog that is extremely unlikely to occur naturally. Like this, for instance:

First Speaker: The Pirates will win the World Series this year.
Second Speaker: Yes, and Da Vinci Code is the best movie ever made.

The police ask us for permission to run automated voice recognition algorithms on all phone conversations, and to record all conversations that contain this verbal handshake. Is it reasonable to give permission?

If the voice recognition is sufficiently accurate, this could be reasonable – even though the wiretapping is not based on advance suspicion of any particular individual. Suspicion is based not on the identity of the individuals speaking, but on the content of the communication. (You could try arguing that the content causes individualized suspicion, at the moment it is analyzed, but if you go that route the individualized suspicion principle doesn’t mean much anymore.)

Obviously we wouldn’t give the police carte blanche to use any kind of content-based suspicion whenever they wanted. What makes this hypothetical different is that the suspicion, though content-based, is narrowly aimed and is based on specific evidence. We have good reason to believe that we’ll be capturing some criminal conversations, and that we won’t be capturing many noncriminal ones. This, I think, is the general principle: intercepted communications may only be made known to a human based on narrowly defined triggers (whether individual-based or content-based), and those triggers must be justified based on specific evidence that they will be fruitful but not overbroad.

You might argue that if the individualized suspicion principle has been good enough for the past [insert large number] years, it should be good enough for the future too. But I think this argument misses an important consequence of changing technology.

Back before the digital revolution, there were only two choices: give the police narrow warrants to search or wiretap specific individuals or lines, or give the police broad discretion to decide whom to search or wiretap. Broad discretion was problematic because the police might search too many people, or might search people for the wrong reasons. Content-based triggering, where a person got to overhear the conversation only if its content satisfied specific trigger rules, was not possible, because the only way to tell whether the trigger was satisfied was to have a person listen to the conversation. And there was no way to unlisten to that conversation if the trigger wasn’t present. Technology raises the possibility that automated algorithms can implement triggering rules, so that content-based triggers become possible – in theory at least.

Given that content-based triggering was infeasible in the past, the fact that traditional rules don’t make provision for it does not, in itself, end the argument. This is the kind of situation that needs to be evaluated anew, with proper respect for traditional principles, but also with an open mind about how those principles might apply to our changed circumstances.

By now I’ve convinced you, I hope, that there is a plausible argument in favor of allowing government to wiretap based on content-based triggers. There are also plausible arguments against. The strongest ones, I think, are (1) that content-based triggers are inconsistent with the current legal framework, (2) that content-based triggers will necessarily make too many false-positive errors and thereby capture too many innocent conversations, and (3) that the infrastructure required to implement content-based triggers creates too great a risk of abuse. I’ll wrap up this series with three more posts, discussing each of these arguments in turn.

For the past several weeks I’ve been writing, on and off, about how technology enables new types of wiretapping, and how public policy should cope with those changes. Having laid the groundwork (1; 2; 3; 4; 5) we’re now ready for to bite into the most interesting question. Suppose the government is running, on every communication, some algorithm that classifies messages as suspicious or not, and that every conversation labeled suspicious is played for a government agent. When, if ever, is government justified in using such a scheme?

Many readers will say the answer is obviously “never”. Today I want to argue that that is wrong – that there are situations where automated flagging of messages for human analysis can be justified.

A standard objection to this kind of algorithmic triggering is that authority to search or wiretap must be based on individualized suspicion, that is, that there must be sufficient cause to believe that a specific individual is involved in illegal activity, before that individual can be wiretapped. To the extent that that is an assertion about current U.S. law, it doesn’t answer my question – recall that I’m writing here about what the legal rules should be, not what they are. Any requirement of individualized suspicion must be justified on the merits. I understand the argument for it on the merits. All I’m saying is that that argument doesn’t win by default.

One reason it shouldn’t win by default is that individualized suspicion is sometimes consistent with algorithmic recognition. Suppose that we have strong cause to believe that Mr. A is planning to commit a terrorist attack or some other serious crime. This would justify tapping Mr. A’s phone. And suppose we know Mr. A is visiting Chicago but we don’t know exactly where in the city he is, and we expect him to make calls on random hotel phones, pay phones, and throwaway cell phones. Suppose further that the police have good audio recordings of Mr. A’s voice.

The police propose to run automated voice recognition software on all phone calls in the Chicago area. When the software flags a recording as containing Mr. A’s voice, that recording will be played for a police analyst, and if the analyst confirms the voice as Mr. A’s, the call will be recorded. The police ask us, as arbiters of the public good, for clearance to do this.

If we knew that the voice recognition algorithm would be 100% accurate, then it would be hard to object to this. Using an automated algorithm would be more consistent with the principle of individualized suspicion than would be the traditional approach of tapping Mr. A’s home phone. His home phone, after all, might be used by an innocent family member or roommate, or by a plumber working in his house

But of course voice recognition is not 100% accurate. It will miss some of Mr. A’s calls, and it will incorrectly flag some calls by others. How serious a problem is this? It depends on how many errors the algorithm makes. The traditional approach sometimes records innocent people – others might use Mr. A’s phone, or Mr. A might turn out to be innocent after all – and these errors make us cautious about wiretapping but don’t preclude wiretapping if our suspicion of Mr. A is strong enough. The same principle ought to hold for automated voice recognition. We should be willing to accept some modest number of errors, but if errors are more frequent we ought to require a very strong argument that recording Mr. A’s phone calls is of critical importance.

In practice, we would want to set out crisply defined criteria for making these determinations, but we don’t need to do that exercise here. It’s enough to observe that given sufficiently accurate voice recognition technology – which might exist some day – algorithmically triggered recording can be (a) justified, and (b) consistent with the principle of individualized suspicion.

But can algorithmic triggering be justified, even if not based on individualized suspicion? I’ll argue next time that it can.

Suppose the government were gathering information about your phone calls: who you talked to, when, and for how long. If that information were made available to human analysts, your privacy would be impacted. But what if the information were made available only to computer algorithms?

A similar question arose when Google introduced its Gmail service. When Gmail users read their mail, they see advertisements. Servers at Google select the ads based on the contents of the email messages being displayed. If the email talks about camping, the user might see ads for camping equipment. No person reads the email (other than the intended recipient) – but Google’s servers make decisions based on the email’s contents.

Some people saw this as a serious privacy problem. But others drew a line between access by people and by computers, seeing access by even sophisticated computer algorithms as a privacy non-event. One person quipped that “Worrying about a computer reading your email is like worrying about your dog seeing you naked.”

So should we worry about the government running computer algorithms on our call data? I can see two main reasons to object.

First, we might object to the government gathering and storing the information at all, even if the information is not (supposed to be) used for anything. Storing the data introduces risks of misuse, for example, that cannot exist if the data is not stored in the first place.

Second, we might object to actions triggered by the algorithms. For example, if the algorithms flag certain records to be viewed by human analysts, we might object to this access by humans. I’ll consider this issue of algorithm-triggered access in a future post – for now, I’ll just observe that the objection here is not to the access by algorithms, but to the access by humans that follows.

If these are only objections to algorithmic analysis of our data, then it’s not the use of computer algorithms that troubles us. What really bothers us is access to our data by people, whether as part of the plan or as unplanned abuse.

If we could somehow separate the use of algorithms from the possibility of human-mediated privacy problems, then we could safely allow algorithms to crawl over our data. In practice, though, algorithmic analysis goes hand in hand with human access, so the question of how to apportion our discomfort is mostly of theoretical interest. It’s enough to object to the possible access by people, while being properly skeptical of claims that the data is not available to people.

The most interesting questions about computerized analysis arise when algorithms bring particular people and records to the attention of human analysts. That’s the topic of my next post.