Landyn Rookard (Loyola University New Orleans College of Law) has posted Inherently Human Functions (100 Tulane Law Review (forthcoming)) on SSRN. Here is the abstract:
Federal law prohibits agencies from outsourcing "inherently governmental functions," defined as functions that are "so intimately related to the public interest as to require performance by Federal Government employees." Behind that short definition are decades of evolving practices and guidance that have frequently vexed agencies attempting to comply with the directive. Despite the shortcomings of the inherently governmental functions framework, this Article argues that Congress and the Executive Branch should establish a similar designation for "inherently human functions," one that aims to combat inappropriate governmental algorithmization and guarantees a human decisionmaker under certain circumstances.
Despite some suggestions in the literature that the inherently governmental functions designation was inspired by the constitutional prohibition on private delegation, the historical record shows that Congress and the Executive Branch developed the designation to address policy concerns about the effect that outsourcing can have on the independence, accountability, and capacity of the federal government. Algorithmic governance poses similar threats and warrants similar safeguards.
The proposed inherently human function designation should learn from the shortcomings of the inherently governmental function designation. First, it should embrace a bottom-up process for filling in the details of the framework, one that prioritizes the voices of groups most directly impacted by algorithmization. Second, the definition should focus on protecting against algorithmization of functions that could cause lasting, difficult-to-remediate harm to individuals' wellbeing. Finally, the inherently human functions designation should be backed by robust public and private enforcement mechanisms, such as ombuds offices, inspectors general, and a private right of action for individuals directly harmed by inappropriate algorithmization.
Recommended.
