EDITOR’S NOTE: This is the first day in a 3-part series on Artificial Intelligence. This project was funded by a grant from the Pulitzer Center and provided by Eye on Ohio, the nonprofit, nonpartisan Ohio Center for Journalism. Knox Pages has joined a collaborative agreement with Eye on Ohio. Please join the free mailing lists for Eye on Ohio as this helps provide more public-service reporting.
Meredith Broussard notes in her book, “Artificial Unintelligence,” that “AI” is a bit of a misnomer. True artificial intelligence means computers have finally achieved consciousness. Scientists are a long way away – if that is even possible.
Why then has “artificial intelligence” become ubiquitous? Major companies and the state of Ohio refer to AI as vital to speech recognition, self-driving cars and web searches. It’s essentially become shorthand for various machine-learning methods to solve a problem which a human can’t easily solve.
For example: A programmer has to code image-recognition software to identify dogs in pictures. How can the programmer explain to a computer what a dog is? Chihuahuas are dogs, and so are Great Danes. But not wolves, which look a lot like dogs, or foxes.
The programmer instead could use thousands of pictures of animals labeled “dogs” and “foxes” and have an AI algorithm learn which are which. The computer compares patterns of each animal’s eyes, nose and snout to see which sizes and shapes are a “dog.” The code tells the computer to decide a shape, such as a dog ear, is more likely a dog.
As François Chollet and J.J. Allaire wrote in their book, Deep Learning with R, from a geometric standpoint, the computer is trying to see how to fold a piece of paper so that the maximum number of data points can be included.
Counterintuitively though, extremely high accuracy is not an end goal because of “overfitting.” A model that follows data too closely might not be good at making predictions in new data it hasn’t seen before. If your dog dataset has too few Chihuahuas and not enough Great Danes, you might miss bigger dogs later.
Machine learning is powerful because it flips the script on computer programming: instead of telling the machine what’s important, programmers study various outcomes to see what’s important. Then they test for better outcomes.
For the past year, Eye on Ohio has been working on an AI project to see how cities and land banks choose to take over decrepit properties. This effort involved hundreds public-records act requests, 5,225 lines of code and countless hours of planning, researching, programming, writing, fact-checking and editing.
Countless articles chronicle rising housing prices. Eye on Ohio wanted to look at the opposite end of the spectrum: What happens to the worst housing? How does that impact people who are struggling?
In Ohio, the county keeps delinquent property lists showing which owners did not pay taxes the previous year – and how much they owe. An auditor’s website lets anyone see property value and payment history.
Most delinquent taxpayers eventually pay back their taxes. But Eye on Ohio started here for several reasons.
First, it would be impractical to study every property in a county to see which might be eligible for a land bank. The delinquent-taxpayer list is a public record which represents virtually all decrepit properties in a county.
Second, delinquent property owners are the biggest funders of land banks in the first place. County Treasurers and Prosecutors split 5 percent of delinquent tax revenue between them in a delinquent tax and assessment collection fund (DTAC.) When a county establishes a land bank, they use those funds. County commissioners can authorize up to 5 percent more.
When a taxpayer becomes delinquent, the Treasurer usually will set up a payment plan. If that fails, the government will sell their tax lien to a third party. But sometimes not even that is successful, particularly for abandoned properties where it can be difficult to first find an owner’s heirs or successors in interest.
Land banks get properties in a variety of ways. Someone can give them a parcel outright or deed property in lieu of foreclosure. But usually, they remediate properties that are way behind on their taxes in the first place.
The thrust of the project is: Of all delinquent properties in a county, which ones go to the land bank? Each has a policy that essentially says, “We try to do the best we can with our limited budget.” What exactly does that mean, mathematically?
Land banks are a great but very limited program. How do officials choose which of the relatively small number of properties they will foreclose upon or demolish?
During the height of the mortgage crisis, many counties got federal funds to supplement their budgets. That money is now largely gone. How will land banks treat rusting properties with a smaller budget?
