I’ve been hesitant to post this video of Keith Olbermann’s 17-minute timeline connecting the shifting terror alert level to the news cycle and administration at the risk of veering too far into politics, but I’m reminded again of it with Tom Ridge essentially admitting to it in his book:

In The Test of Our Times: America Under Siege, Ridge wrote that although Rumsfeld and Ashcroft wanted to raise the alert level, “There was absolutely no support for that position within our department. None. I wondered, ‘Is this about security or politics?'”

Only to recant and be taken to task by Rachel Maddow:

Ridge went on to say that “politics was not involved” and that “I was not pressured.” Maddow then read to Ridge directly from his book’s jacket: “‘He recounts episodes such as the pressure that the DHS received to raise the security alert on the eve of of the ’04 presidential election.’ That’s wrong?”

As Seth Meyers put it, “My shock level on manipulation of terror alerts for political gain is green, or low.”

At any rate, whether there is in fact correlation, causation, or simply a conspiracy theory that gives far too much credit to the number of people who would have to be involved, I think it’s an interesting look at 1) message control 2) using the press (or a clear example of the possibilities) 3) the power of assembling information like this to produce such a timeline, and 4) actual reporting (as opposed to tennis match commentary) done by a 24-hour news channel.

Of course, I was disappointed that it wasn’t an actual visual timeline, though somebody has probably done that as well.

There’s simply no way to give people access to others’ private records — in the name of security or otherwise — and trust those given access to do the right thing. From a New York Times story on the NSA’s expanded wiretapping:

The former analyst added that his instructors had warned against committing any abuses, telling his class that another analyst had been investigated because he had improperly accessed the personal e-mail of former President Bill Clinton.

This is not isolated, and this will always be the case. From a story in The Boston Globe a month ago:

Law enforcement personnel looked up personal information on Patriots star Tom Brady 968 times – seeking anything from his driver’s license photo and home address, to whether he had purchased a gun – and auditors discovered “repeated searches and queries” on dozens of other celebrities such as Matt Damon, James Taylor, Celtics star Paul Pierce, and Red Sox owner John Henry, said two state officials familiar with the audit.

The NSA wiretapping is treated too much like an abstract operation, with most articles that describe it overloaded with talk of “data collection,” and “monitoring,” and the massive scale of data that traffics through internet service providers. But the problem isn’t the computers and data and equipment, it’s that on the other end of the line, a human being is sitting there deciding what to do with that information. Our curiosity and voyeurism leaves us fundamentally flawed for dealing with such information, and unable to ever live up to the responsibility of having that access.

The story about the police officers who are overly curious about sports stars (or soft rock balladeers) is no different from the NSA wiretapping, because it’s still people, with the same impulses, on the other end of the line. Until reading this, I had wanted to believe that NSA employees — who should truly understand the ramifications — would have been more professional. But instead they’ve proven themselves no different from a local cop who wants to know if Paul Pierce owns a gun or Matt Damon has a goofy driver’s license picture.

Some thoughtful comments passed along by Alex Hutton regarding the last post:

Part of the problem with point technology solutions is in the policies of implementation. IMHO, we undervalue the subject matter expert, or operate as a denigrated bureaucracy which does not allow the subject matter expert the flexibility to make decisions. When that happens, the decision is left to technology (and as you point out, no technology is a perfect decision maker).

I thought it was apropos that you brought in the Schneier example. I’ve been very much involved in a parallel thought process in the same industry as he, and we (my partner and I) are coming to a solution that attempts to balance technology, point human decision, and the bureaucracy within which they operate.

If you believe the Bayesians, then the right Bayesian network mimics the way the brain processes qualitative information to create a belief (or in the terms of Bayesians, a probability statement used to make a decision). As such, the current way we use the technology (that policy of implementation, above) is faulty because it minimizes that “Human Computational Engine” for a relatively unsophisticated, unthinking technology. That’s not to say that technologies like facial recognition are worthless – computational engines, even less magic ones that aren’t 99.99% accurate, are valid pieces of prior information (data).

Now in the same way, Human Computational Engines are also less than perfectly accurate. In fact, they are not at all guaranteed to work the same way twice – even by the same person unless that person is using framework to provide rigor, rationality, and consistency in analysis.

So ideally, in physical security (or information security where Schneier and I come from) the imperfect computer detection engine is combined with a good Bayesian network and well trained/educated/experienced subject matter experts to create a more accurate probability statement around terrorist/non-terrorist – one that at least is better at identifying cases where more information is needed before a person is prevented from flying, searched and detained. While this method, too, would not be 100% infallible (no solution will ever be), it would create a more accurate means of detection by utilizing the best of the human computational engine.

I believe the Bayesians, just 99.99% of the time.

Computers are really good at repetitive work. You can ask a computer to multiply two numbers together seven billion times and not only will it not complain, it’ll probably have seven billion answers for you a few seconds later. Ask a person to do the same thing and they’ll either walk away at the outset, realizing the ridiculousness of the task, or they’ll get through the first few tries and lose interest. But even the fact that a human can recognize the ridiculousness of the task is important. Humans are good at lots of things—like identifying a face in a crowd—that cannot be addressed by computation with the same level of accuracy.

Visualization is about the interface between what humans are good at, and what computers are good at. First, the computer can crunch all seven billion numbers, then present the results in a way that we can use our own perceptual skills to identify what’s important or interesting. (This is also why the design of a visualization is a fundamentally human task, and not something to be left to automation.)

This is also the subject of Luis von Ahn’s work at Carnegie Mellon. You’re probably familiar with CAPTCHA images—usually wavy numbers and letters that you have to discern when signing up for a webmail account or buying tickets from Ticketmaster. The acronym stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart,” a clever mouthful referring to Alan Turing’s work in discerning man or machine. (I encourage you to read about them, but this is already getting long so I won’t get into it here.)

More interesting than CAPTCHA, however, is the whole notion that’s behind it: that it’s an example of relying on humans to do what they’re best at, though it’s a task that’s difficult for computers. (Sure, in recent weeks, people have actually found ways to “break” CAPTCHAs in specific cases, but that’s not important here.) For instance, the work was extended to the Google Image Labeler, described as follows:

You’ll be randomly paired with a partner who’s online and using the feature. Over a two-minute period, you and your partner will:

• View the same set of images.
• Provide as many labels as possible to describe each image you see.
• Receive points when your label matches your partner’s label. The number of points will depend on how specific your label is.
• See more images until time runs out.

Prior to this, most image labeling systems had to do with getting volunteers to name or tag images individually. As you can imagine, the quality of tags suffer considerably because of everything from differences in how people perceive or describe what they see, to individuals who try to be a little too clever in choosing tags. With the Image Labeler game, that’s turned around backwards, where there is a motivation to use tags that match the other person, thus minimizing the previous problems. (It’s “Mechanical Turk” meets “Family Feud”.) They’ve also applied the same ideas to scanning books—where fragments of text that cannot be recognized by software are instead checked by multiple people.

More recently, von Ahn’s group has expanded these ideas in Games With A Purpose, a site that addresses these “casual games” more directly. The new site is covered in this New Scientist article, which offers additional tidbits (perspective? background? couldn’t think of the right word).

You can also watch Luis’ Google Tech Talk about Human Computation, which if I’m not mistaken, led to the Image Labeler project.

(We met Luis a couple times while at CMU and watched the Superbowl with his awesome fiancée Laura, cheering on her hometown Chicago Bears against those villainous Colts. We were happy when he received a MacArthur Fellowship for his work—just the sort of person you’d like to get such an award that highlights people who often don’t quite fit in their field.)

Returning to the earlier argument, algorithms to identify a face in a crowd are certainly improving. But without a significant breakthrough, their usefulness will be significantly limited. One commonly hyped use for such systems is airport security. Bruce Schneier explains the problem:

Suppose this magically effective face-recognition software is 99.99 percent accurate. That is, if someone is a terrorist, there is a 99.99 percent chance that the software indicates “terrorist,” and if someone is not a terrorist, there is a 99.99 percent chance that the software indicates “non-terrorist.” Assume that one in ten million flyers, on average, is a terrorist. Is the software any good?

No. The software will generate 1000 false alarms for every one real terrorist. And every false alarm still means that all the security people go through all of their security procedures. Because the population of non-terrorists is so much larger than the number of terrorists, the test is useless. This result is counterintuitive and surprising, but it is correct. The false alarms in this kind of system render it mostly useless. It’s “The Boy Who Cried Wolf” increased 1000-fold.

Given the number of travelers at Boston Logan in 2006, that would be two “terrorists” identified per day. (And with Schneier’s one in ten million is a terrorist figure, that would be two or three terrorists per year…clearly too generous, which makes the face detection accuracy even worse than how he describes it.) I find myself thinking about the 99.99% accuracy number as I stare at the back of heads lined up at the airport security checkpoint—itself a human problem, not a computational problem.