Online Dating, Sex Offenders and Background Checks: The Hype and The Problem

Via PogoWasRight, I hear of this NJ online dating bil:

The bill as introduced requires online dating services to disclose to any user from New Jersey whether it has performed background checks on members of the site.

The flawed part of the bill comes in the fact that to satisfy the bill’s “Criminal Background screening” all a site has to do is a simple name search via a regularly updated government public records database or a database maintained by a private vendor.

The actual text of the bill is available in PDF.

The article calls the bill “flawed,” and I agree. These sorts of simple name matching background checks are unreliable. They’re likely to have errors, they’re easy to fool, they’re likely to have mismatches, and they promote a false sense of security. They may not be complete, arrests are not followed up by lack of charges, or something else that shows a person is innocent. Expungements and pardons may not only fail to clear the record, they may not be returned at all. All that and in general criminal records are actually hard to read: its difficult for a lay person to tell from a court printout what someone’s exact criminal history is.

I wanted to add what sorts of things help promote not only the hype, but the background check solution.

As the article notes, Wired Editor Kevin Poulson wrote a perl script to compare sex offender lists with names on MySpace. They ended up arresting an individual from that, and Wired wrote it up under the headline MySpace Predator Caught By Code.

At the time, I blogged about this on my previous blog:

Wired wrote some code to match the information in the national sex offender database — first and last name, and zip code (within 5 miles) — with profiles on MySpace. This gave them “vast numbers of false or unverifiable matches.” It took months of part time work, looking at each profile, to figure out which were actual predators still using the site for their predation. Some profiles were dormant. Some were innocent. One lead to an arrest.

But here was the problem. Not just with the process, but with the entire pitch:

The predator was not caught by code. “Vast numbers of false or unverifiable” matches were caught by code.

It was the human work of tracking down all the false matches and doing investigations that actually caught the bad guy. I predicted at the time that this exercise would incorrectly portray the “magic” of data matching. Not only does it promote the hype of sex predators on social network sites, but it also promotes the idea that there is an easy “search” that one can make to check this threat.

Posted: November 27, 2007 in: