Remember the good old
days when the one thing almost everyone interested in child welfare could agree
on was “home visiting”? Assign a trained worker to help “at-risk” new parents,
sometimes even before the child’s birth. Then follow up with regular visits to
the home.
When such programs follow
a particular model, the Nurse
Family Partnership, this kind of help actually is helpful.
Those
whose top priority really is taking away more children find this acceptable since
it widens, rather than narrows, the net of intervention into families.
Advocates of family preservation find it acceptable because it’s strictly
voluntary for the families.
About
the only people to object, initially, were right-wingers with their paranoid
ideas that this was a sneaky backdoor way for “big gummint” to spy on families.
Just because you’re paranoid…
But
then along came self-proclaimed liberal Elizabeth Bartholet, who proved the
adage “just because you’re paranoid, doesn’t mean they’re not out to get you.”
Based on the criteria she sets out in her book, “Nobody’s Children,” her agenda
is so extreme that I estimate it would require taking at least one million
children from their parents every year.
That would be even before
implementing a linchpin of her strategy, which I discussed last month: a very different version of home visiting. The Bartholet
version is the right-wing nightmare come true: It would be universal and
mandatory. In other words, a government-mandated spy in every living room.
Bartholet specifies that the visitors would be mandatory child abuse reporters,
and that the purpose of those visits includes “surveillance.” Indeed, that
seems to be their primary purpose.
Of course that’s not
going to happen – not because it’s Orwellian, but because it would be so
expensive. But now, it turns out, Bartholet’s vision also was hopelessly
low-tech. In the age of the latest fad in child welfare, predictive
analytics, it no longer matters what the spy in the living room actually
sees. Just making use of a home visiting program ratchets up the level of suspicion.
At least that seems to be
the latest plan from software vendor SAS, according to a recent story from predictive analytics evangelist and
Bartholet disciple Daniel Heimpel, publisher of The
Chronicle of Social Change.
SAS is the company which
used past cases to test a secret, proprietary predictive analytics algorithm in
Los Angeles. SAS proclaimed it a rousing success even though 95 percent of the time when the algorithm
predicted severe harm to a child the algorithm was wrong.
Now SAS is developing
a new approach in Florida. This one targets poor
people. It compares birth records to three other databases: child welfare
system involvement, public assistance and “mothers who had been involved in the
state’s home visiting program.”
So
listen up “at-risk” new mothers: In the world of predictive analytics, the fact
that you reached out for help when you needed it and accepted assistance on how
to be better parents isn’t a sign of strength – it’s a reason to consider you
suspect, and make it more likely that your children will be taken away.
So the conclusion is
obvious: Mothers will have to turn down the help in order to protect their
children from the risk of having to face the horrors of foster care. Oh,
wait, that probably won’t work. Big Data entrepreneurs would simply respond by
finding a database listing mothers who refuse home
visiting, and count that against them too.
The
only way to escape Big Data is to hide the pregnancy, avoid prenatal care and
give birth at home. Yes, child welfare has found one more way to endanger
children in the name of protecting them.
A dilemma for predictive analytics defenders
SAS’ approach also should
create a dilemma for some defenders of predictive analytics, such as Heimpel, who can’t see why
anyone would object to using it for targeting which families should get help,
in particular home visiting. SAS is standing that on its head. Their new
approach winds up using home visiting to target investigations.
SAS’
evidence that its new approach works consists of pointing out that a lot of
those it targeted had repeat reports of child abuse. But the reasoning is
circular. Part of the rationale for predictive analytics is that decisions now
are too subjective and prone to bias. Logically, then, you can’t turn around
and cite those same subjective, biased decisions as proof your approach works.
Indeed, reliance on prior
reports as proof of accuracy was a key flaw in evaluations of New Zealand’s experiment with predictive
analytics.
And
you’re certainly not going to make the system less biased by tracking only poor
people. That only magnifies the existing “surveillance bias” that makes poor
people targets because they’re more visible to government agencies.
But
hey, the news isn’t bad for everyone. Middle class child abusers are in luck!
If child welfare systems adopt this latest Big Data brainstorm, middle class
child abusers are likely to be a lower priority for investigations – and likely
to have lower risk scores when a caseworker shows up at the door.
As for
poor people who need help with child-rearing, I guess they’ll just have to hire
nannies.
