inkbluesky

Why Data Mining Won’t Stop Terrorists

This unrealistically-accurate system will generate one billion false alarms for every real terrorist plot it uncovers. Every day of every year, the police will have to investigate 27 million potential plots in order to find the one real terrorist plot per month. Raise that false-positive accuracy to an absurd 99.9999% and you’re still chasing 2,750 false alarms per day — but that will inevitably raise your false negatives, and you’re going to miss some of those ten real plots.

This isn’t anything new. In statistics, it’s called the “base rate fallacy,” and it applies in other domains as well. For example, even highly accurate medical tests are useless as diagnostic tools if the incidence of the disease is rare in the general population. Terrorist attacks are also rare, any “test” is going to result in an endless stream of false alarms.

This is exactly the sort of thing we saw with the NSA’s eavesdropping program: the New York Times reported that the computers spat out thousands of tips per month. Every one of them turned out to be a false alarm.

And the cost was enormous: not just the cost of the FBI agents running around chasing dead-end leads instead of doing things that might actually make us safer, but also the cost in civil liberties. The fundamental freedoms that make our country the envy of the world are valuable, and not something that we should throw away lightly.

Data mining can work. It helps Visa keep the costs of fraud down, just as it helps Amazon.com show me books that I might want to buy, and Google show me advertising I’m more likely to be interested in. But these are all instances where the cost of false positives is low — a phone call from a Visa operator, or an uninteresting ad — and in systems that have value even if there is a high number of false negatives.

Finding terrorism plots is not a problem that lends itself to data mining. It’s a needle-in-a-haystack problem, and throwing more hay on the pile doesn’t make that problem any easier. We’d be far better off putting people in charge of investigating potential plots and letting them direct the computers, instead of putting the computers in charge and letting them decide who should be investigated.

Most Human-chimp Differences Due To Gene Regulation — Not Genes

The vast differences between humans and chimpanzees are due more to changes in gene regulation than differences in individual genes themselves, researchers from Yale, the University of Chicago, and the Hall Institute in Parkville, Victoria, Australia, argue in the 9 March 2006 issue of the journal Nature.

The scientists provide powerful new evidence for a 30-year-old theory, proposed in a classic paper from Mary-Claire King and Allan Wilson of Berkeley. That 1975 paper documented the 99-percent similarity of genes from humans and chimps and suggested that altered gene regulation, rather than changes in coding, might explain how so few genetic changes could produce the wide anatomic and behavioral differences between the two.

Using novel gene-array technology to measure the extent of gene expression in thousands of genes simultaneously, this study shows that as humans diverged from their ape ancestors in the last five million years, genes for transcription factors — which control the expression of other genes — were four times as likely to have changed their own expression patterns as the genes they regulate.

Because they influence the activity of many “downstream” genetic targets, small changes in the expression of these regulatory genes can have an enormous impact.

“When we looked at gene expression, we found fairly small changes in 65 million years of the macaque, orangutan, and chimpanzee evolution,” said study author Yoav Gilad, Ph.D., assistant professor of human genetics at the University of Chicago, “followed by rapid change, along the five million years of the human lineage, that was concentrated on these specific groups of genes. This rapid evolution in transcription factors occurred only in humans.”