Tomorrow’s government workers aren’t your neighbors. They’re
algorithms.
Federal and local governments increasingly are turning to
data-trained models and artificial intelligence to help make decisions
— big decisions, such as the length of a prison sentence, and smaller
decisions, such as when to deploy health inspectors to restaurants. But these
algorithms need oversight and it is nowhere to be found.
Congress should act on this critical issue, but so, too,
should local governments, where some of the most consequential uses of
government by algorithm can be found.
In Pittsburgh, for example, the city and county governments
are using algorithms to perform a variety of functions. Like many localities
across the country, algorithms are helping judges determine who gets bail and
who does not. They are guiding police about where and when to send patrols.
They are helping child welfare hotline screeners determine when to send
in a case worker. And they’re only becoming more common.
Each of these applications has a worthy goal: public safety,
preventing crime, and saving children from neglect, abuse or worse. It is
possible that some of these algorithms are “better” at doing so than the humans
who might be making decisions in their place. Algorithms can be programmed to
take more evidence into account; they can do more, faster. The promise of big
data and predictive analytics for improving our lives and society should not be
ignored.
However, it is also possible that these algorithms could
perpetuate — or even accelerate — existing discrimination patterns. Ask Amazon,
whose now-discarded interview system trained itself to avoid
selecting resumes that indicated the applicants were women. If the data that
helped train policing patrol models is using years of notoriously biased data —
such as information about historical arrests — how have we ensured that the
algorithm doesn’t churn out biased outcomes?
Yet too many of these algorithms operate like black boxes,
with little to no means for the public and researchers to scrutinize how
decisions are made. As algorithms increasingly are used for determining
government services and benefits, or even whether or not someone goes to prison,
what remedies do individuals have if the algorithm is biased?
We cannot rely just on good intentions, or even impressive
processes and evaluations within agencies. Instead, we should want public
oversight to ensure accountability and fairness. No jurisdiction in the United
States has successfully grappled with the complexities of how to adapt to this
new administrative state by algorithm, though some are trying, including New York City and Washington State.
To be sure, this is no easy task. This is why the University
of Pittsburgh Institute for Cyber Law, Policy and Security — Pitt Cyber — has
formed the Pittsburgh
Task Force on Public Algorithms to study oversight of local public
algorithms. With support from The Heinz Endowments, we have gathered experts
and community leaders to develop best practices and issue practical guidance
for local policymakers wishing to take full advantage of the promise of data,
while still ensuring accountability and equity for all residents. This task
force will not work in isolation; we also will be working throughout the
western Pennsylvania region to ensure that we learn from residents across
communities.
In a country where techno-optimism long has ruled, it is
time we also ensure accountability and fairness.
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