  As algorithms become an influential component of government decision-making
around the world, policymakers have debated how governments can attain the
benefits of algorithms while preventing the harms of algorithms. One mechanism
that has become a centerpiece of global efforts to regulate government
algorithms is to require human oversight of algorithmic decisions. Despite the
widespread turn to human oversight, these policies rest on an uninterrogated
assumption: that people are able to effectively oversee algorithmic
decision-making. In this article, I survey 41 policies that prescribe human
oversight of government algorithms and find that they suffer from two
significant flaws. First, evidence suggests that people are unable to perform
the desired oversight functions. Second, as a result of the first flaw, human
oversight policies legitimize government uses of faulty and controversial
algorithms without addressing the fundamental issues with these tools. Thus,
rather than protect against the potential harms of algorithmic decision-making
in government, human oversight policies provide a false sense of security in
adopting algorithms and enable vendors and agencies to shirk accountability for
algorithmic harms. In light of these flaws, I propose a shift from human
oversight to institutional oversight as the central mechanism for regulating
government algorithms. This institutional approach operates in two stages.
First, agencies must justify that it is appropriate to incorporate an algorithm
into decision-making and that any proposed forms of human oversight are
supported by empirical evidence. Second, these justifications must receive
democratic public review and approval before the agency can adopt the
algorithm.
