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How I Became a Policy Wonk

March 9, 2007 by

It’s All-Request Friday, when I blog on topics suggested by readers. David Molnar writes,

I’d be interested to hear your thoughts on how your work has come to have significant interface with public policy questions. Was this a conscious decision, did it “just happen,” or somewhere in between? Is this the kind of work you thought you’d be doing when you first set out to do research? What would you do differently, if you could do it again, and what in retrospect were the really good decisions you made?

I’ll address most of this today, leaving the last sentence for another day.

When I started out in research, I had no idea public policy would become a focus of my work. The switch wasn’t so much a conscious decision as a gradual realization that events and curiosity had led me into a new area. This kind of thing happens all the time in research: we stumble around until we reach an interesting result and then, with the benefit of hindsight, we construct a just-so story explaining why that result was natural and inevitable. If the result is really good, then the just-so story is right, in a sense – it justifies the result and it explains how we would have gotten there if only we hadn’t been so clueless at the start.

My just-so story has me figuring out three things. (1) Policy is deep and interesting. (2) Policy affects me directly. (3) Policy and computer security are deeply connected.

Working on the Microsoft case first taught me that policy is deep and interesting. The case raised obvious public policy issues that required deep legal, economic, and technical thinking, and deep connections between the three, to figure out. As a primary technical advisor to the Department of Justice, I got to talk to top-notch lawyers and economists about these issues. What were the real-world consequences of Microsoft doing X? Would would be the consequences if they were no longer allowed to do Y? Theories weren’t enough because concrete decisions had to be made (not by me, of course, but I saw more of the decision-making process than most people did). These debates opened a window for me, and I saw in a new way the complex flow from computer science in the lab to computer products in the market. I saw, too, how public policy modulates this flow.

The DMCA taught me that policy affects me directly. The first time I saw a draft of the DMCA, before it was even law, I knew it would mean trouble for researchers, and I joined a coalition of researchers who tried to get a research exemption inserted. The DMCA statute we got was not as bad as some of the drafts, but it was still problematic. As fate would have it, my own research triggered the first legal battle to protect research from DMCA overreaching. That was another formative experience.

The third realization, that policy and computer security are joined at the hip, can’t be tied to any one experience but dawned on me slowly. I used to tell people at cocktail parties, after I had said I work on computer security and they had asked what in the world that meant, that computer security is “the study of who can do what to whom online.” This would trigger either an interesting conversation or an abrupt change of topic. What I didn’t know until somebody pointed it out was that Lenin had postulated “who can do what to whom” (and the shorthand “who-whom”) as the key question to ask in politics. And Lenin, though a terrible role model, did know a thing or two about political power struggles.

More to the point, it seems that almost every computer security problem I work on has a policy angle, and almost every policy problem I work on has a computer security angle. Policy and security try, by different means, to control what people can do, to protect people from harmful acts and actors, and to ensure freedom of action where it is desired. Working on security makes my policy work better, and vice versa. Many of the computer scientists who are most involved in policy debates come from the security community. This is not an accident but reflects the deep connections between the two fields.

(Have another topic to suggest for All-Request Friday? Suggest it in the comments here.)

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How Computers Can Make Voting More Secure

March 7, 2007 by

By now there is overwhelming evidence that today’s paperless computer-based voting technologies have such serious security and reliability problems that we should not be using them. Computers can’t do the job by themselves; but what role should they play in voting?

It’s tempting to eliminate computers entirely, returning to old-fashioned paper voting, but I think this is a mistake. Paper has an important role, as I’ll describe below, but paper systems are subject to well-known problems such as ballot-box stuffing and chain voting, as well as other user-interface and logistical challenges.

Security does require some role for paper. Each vote must be recorded in a manner that is directly verified by the voter. And the system must be software-independent, meaning that its accuracy cannot rely on the correct functioning of any software system. Today’s paperless e-voting systems satisfy neither requirement, and the only practical way to meet the requirements is to use paper.

The proper role for computers, then, is to backstop the paper system, to improve it. What we want is not a computerized voting system, but a computer-augmented one.

This mindset changes how we think about the role of computers. Instead of trying to make computers do everything, we will look instead for weaknesses and gaps in the paper system, and ask how computers can plug them.

There are two main ways computers can help. The first is in helping voters cast their votes. Computers can check for errors in ballots, for example by detecting an invalid ballot while the voter is still in a position to fix it. Computers can present the ballot in audio format for the blind or illiterate, or in multiple languages. (Of course, badly designed computer interfaces can do harm, so we have to be careful.) There must be a voter-verified paper record at the end of the vote-casting process, but computers, used correctly, can help voters create and validate that record, by acting as ballot-marking devices or as scanners to help voters spot mismarked ballots.

The second way computers can help is by improving security. Usually the e-voting security debate is about how to keep computers from making security too much worse than it was before. Given the design of today’s e-voting systems, this is appropriate – just bringing these systems up to the level of security and reliability in (say) the Xbox and Wii game consoles would be nice. Even in a computer-augmented system, we’ll need to do a better job of vetting the computers’ design – if a job is worth doing with a computer, it’s worth doing correctly.

But once we adopt the mindset of augmenting a paper-based system, security looks less like a problem and more like an opportunity. We can look for the security weaknesses of paper-based systems, and ask how computers can help to address them. For example, paper-based systems are subject to ballot-box stuffing – how can computers reduce this risk?

Surprisingly, the designs of current e-voting technologies, even the ones with paper trails, don’t do all they can to compensate for the weaknesses of paper. For example, the current systems I’ve seen keep electronic records that are subject to straightforward post-election tampering. Researchers have studied approaches to this problem, but as far as I know none are used in practice.

In future posts, we’ll discuss design ideas for computer-augmented voting.

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Fact check: The New Yorker versus Wikipedia

March 4, 2007 by

In July—when The New Yorker ran a long and relatively positive piece about Wikipedia—I argued that the old-media method of laboriously checking each fact was superior to the wiki model, where assertions have to be judged based on their plausibility. I claimed that personal experience as a journalist gave me special insight into such matters, and concluded: “the expensive, arguably old fashioned approach of The New Yorker and other magazines still delivers a level of quality I haven’t found, and do not expect to find, in the world of community-created content.”

Apparently, I was wrong. It turns out that EssJay, one of the Wikipedia users described in The New Yorker article, is not the “tenured professor of religion at a private university” that he claimed he was, and that The New Yorker reported him to be. He’s actually a 24-year-old, sans doctorate, named Ryan Jordan.

Jimmy Wales, who is as close to being in charge of Wikipedia as anybody is, has had an intricate progression of thought on the matter, ably chronicled by Seth Finklestein. His ultimate reaction (or at any rate, his current public stance as of this writing) is on his personal page in Wikipedia

I only learned this morning that EssJay used his false credentials in content disputes… I understood this to be primarily the matter of a pseudonymous identity (something very mild and completely understandable given the personal dangers possible on the Internet) and not a matter of violation of people’s trust.

As Seth points out, this is an odd reaction since it seems simultaneously to forgive EssJay for lying to The New Yorker (“something very mild”) and to hold him much more strongly to account for lying to other Wikipedia users. One could argue that lying to The New Yorker—and by extension to its hundreds of thousands of subscribers—was in the aggregate much worse than lying to the Wikipedians. One could also argue that Mr. Jordan’s appeal to institutional authority, which was as successful as it was dishonest, raises profound questions about the Wikipedia model.

But I won’t make either of those arguments. Instead, I’ll return to the issue that has me putting my foot in my mouth: How can a reader decide what to trust? I predicted you could trust The New Yorker, and as it turns out, you couldn’t.

Philip Tetlock, a long-time student of the human penchant for making predictions, has found (in a book whose text I can’t link to, but which I encourage you to read) that people whose predictions are falsified typically react by making excuses. They typically claim that they are off the hook because the conditions based on which they predicted a certain result were actually not as they seemed at the time of the inaccurate prediction. This defense is available to me: The New Yorker fell short of its own standards, and took EssJay at his word without verifying his identity or even learning his name. He had, as all con men do, a plausible-sounding story, related in this case to a putative fear of professional retribution that in hindsight sits rather uneasily with his claim that he had tenure. If the magazine hadn’t broken its own rules, this wouldn’t have gotten into print.

But that response would be too facile, as Tetlock rightly observes of the general case. Granted that perfect fact checking makes for a trustworthy story; how do you know when the fact checking is perfect and when it is not? You don’t. More generally, predictions are only as good as someone’s ability to figure out whether or not the conditions are right to trigger the predicted outcome.

So what about this case: On the one hand, incidents like this are rare and tend to lead the fact checkers to redouble their meticulousness. On the other, the fact claims in a story that are hardest to check are often for the same reason the likeliest ones to be false. Should you trust the sometimes-imperfect fact checking that actually goes on?

My answer is yes. In the wake of this episode The New Yorker looks very bad (and Wikipedia only moderately so) because people regard an error in The New Yorker to be exceptional in a way the exact same error in Wikipedia is not. This expectations gap tells me that The New Yorker, warts and all, still gives people something they cannot find at Wikipedia: a greater, though conspicuously not total, degree of confidence in what they read.

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Introducing All-Request Friday

March 1, 2007 by

Adapting an idea from Tyler Cowen, I’m going to try a new feature, where on Fridays I post about topics suggested by readers. Please post your suggested topics in the comments.

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Manipulating Reputation Systems

March 1, 2007 by

BoingBoing points to a nice pair of articles by Annalee Newitz on how people manipulate online reputation systems like eBay’s user ratings, Digg, and so on.

There’s a myth floating around that such systems distill an uncannily accurate folk judgment from the votes submitted by millions of ordinary citizens. The wisdom of crowds, and all that. In fact, reputation systems are fraught with problems, and the most important systems survive because companies expend great effort to supplement the algorithms by investigating abuse and trying to compensate for it. eBay, for example, reportedly works very hard to fight abuse of its reputation system.

Why do people put more faith in reputation systems than the systems really deserve? One reason is the compelling but not entirely accurate analogy to the power of personal reputations in small town gossip networks. If a small-town merchant is accused of cheating a customer, everyone in town will find out quickly and – here’s where the analogy goes off the rails – individual townspeople will make nuanced judgments based on the details of the story, the character of the participants, and their own personal experiences. The reason this works is that the merchant, the customer, and the person evaluating the story are embedded in a complex, densely interconnected network.

When the network of participants gets much bigger and the interconnections much sparser, there is no guarantee that the same system will still work. Even if it does work, a large-scale system might succeed for different reasons than the small-town system. What we need is some kind of theory: some kind of explanation for why a reputation system can succeed. Our theory, whatever it is, will have to account for the desires and incentives of participants, the effect of relevant social norms, and so on.

The incentive problem is especially challenging for recommendation services like Digg. Digg assumes that users will cast votes for the sites they like. If I vote for sites that I really do like, this will mostly benefit strangers (by helping them find something cool to read). But if I sell my votes or cast them for sites run by my friends and me, I will benefit more directly. In short, my incentive is to cheat. These sorts of problems seem likely to get worse as a service grows, because the stakes will grow and the sense of community may weaken.

It seems to me that reputation systems are a fruitful area for technical, economic and social research. I know there is research going on already – and readers will probably chastise me in the comments for not citing it all – but we’re still far from understanding online reputation.

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