Sunday, February 24, 2019

Can child welfare control its “nuclear weapon”? Here’s your chance of a lifetime to find out!

When New York State instituted a lottery in 1967, it came
with all sorts of high-minded promises about how it would
be advertised.  The fate of those promises provides a lesson
concerning whether to believe similar promises about predictive
analytics in child welfare. (Photo by Reuben Whitehouse)

Predictive analytics is the nuclear weapon of child welfare.  Vast amounts of data are taken from people – especially poor people – without their consent (like what Facebook does, only worse). Then if someone alleges that one of those people has committed child abuse or neglect, a secret, or perhaps only semi-secret, algorithm coughs up a risk score. That score is an invisible scarlet number that can brand not only parents, but their children, for life.

As Author Virginia Eubanks explains in her book, Automating Inequality, rather than counteracting the racial and class biases of the human beings who run child welfare systems, it magnifies those biases. She calls it “poverty profiling.” And ProPublica has documented how this has played out in criminal justice.

So what is the response from proponents of analytics in child welfare – a field that is super secretive with no real accountability, due process, or checks and balances?  Endless promises of self-restraint.

Sometimes they say: “We’ll only use it to target prevention programs.”  But we already know how to target prevention programs without an algorithm: Just put them where the poor people are, since the overwhelming majority of cases involve “neglect” and child welfare systems routinely label poverty as neglect.

Or they’ll say: “Child abuse hotline operators will know the “risk score” but we won’t even tell the people who actually go out to investigate the allegation.”  But whoever is going out to investigate knows that if they’re told to get out there in a hurry it’s probably because the risk score was high. So whether they’re told or not, they know.

Or they’ll say: “We’ll never, ever use the score to decide whether to remove a child from the home.”  But again, the caseworker knows (whether explicitly told or not) when the algorithm has rated a case high risk – and they can’t unknow it when the time comes to decide whether to remove the child.

Or they’ll say: “Even if we get our fondest wish and get to slap a ‘risk score’ on every child at birth (and make no mistake, for some in the field, it is their fondest wish) we’ll only use it for prevention.  But – well, see all the problems cited above.

The limits of high-minded promises 

But there’s an even bigger problem with all these high-minded promises. What happens as soon as there’s pressure to be less high-minded?

The amount of pressure needed to get politicians to abandon their principles can be remarkably low – as is illustrated by the story of the New York State Lottery.  Yes, the Lottery.

New York State was among the first in the modern era to institute a lottery, in 1967.  It took an amendment to the State Constitution – so there were lots of high-minded promises to allay concerns of those who feared it would encourage compulsive gambling or encourage those least able to afford it to waste their money.

The key selling point, it was promised, would be an appeal not to greed but to generosity.  Advertising would emphasize that lottery proceeds would be used to help fund public education.  So the first lottery slogan was "Your Chance of a Lifetime to Help Education."  I grew up in New York and I recall an early print ad that said “The New York State Lottery: It’s not the money; it’s the principal. And the teachers. And the students.”

There was just one problem. Not enough people were buying lottery tickets.  Sales were way below projections.  So, by the 1980s, the lottery took a different approach that might best be called, it’s not the principals and the teachers and the students – it’s the money! Money! Money!  Have a look:

Yes, the Lottery still sometimes produces commercials that take the high road, but this is the dominant theme.

If all it takes is revenue falling short of projections to prompt this abandonment of principle (and principals), imagine what would happen in a field where the stakes are a lot higher.

Imagine this scenario:  A child “known to the system” has died.  The media have found out the name of the caseworker who mistakenly thought the home was safe. After being attacked in news accounts and/or by politicians, she comes forward to tell her story.

Choking back tears, she says: “My bosses had an algorithm that told them this family was high-risk. But they never told me. Of course, if only I’d known I never would have left that child there.”

What are the odds that the leader of the child protective services agency would stick to the policy of using predictive analytics with only the utmost restraint?  Even if s/he wanted to, what are the odds that the political leadership in the state or county would allow such restraint to continue?

I’d say you’ve got a better chance of winning the lottery.