Governing Without Decision Makers Algorithmic Management and the Structural Failure of U.S. Employment Law

Algorithmic management has become a central mechanism through which employers exercise control over work. Across sectors, automated systems now allocate tasks, evaluate performance, and trigger discipline, including termination. These systems do not merely assist managerial decision-making; they replace it.

This Article argues that the rise of algorithmic management reveals a structural failure in U.S. employment law. Existing legal frameworks presuppose identifiable decision-makers, accessible reasoning, and contestable decision-making processes. In algorithmically managed workplaces, however, employment outcomes are produced by distributed and partially opaque systems that break the link between decision and responsibility.

As a result, core doctrines-including anti-discrimination law under Title VII-remain formally applicable but become increasingly difficult to enforce in practice. Workers may retain legal rights while facing significant barriers to identifying decision-makers, accessing decision criteria, and challenging adverse outcomes. This Article develops this claim by analyzing how algorithmic systems restructure allocation, evaluation, and enforcement in the workplace, and by examining the resulting breakdown of attribution, transparency, and contestability.

It then advances a framework for restoring accountability through enforceable transparency obligations, meaningful human oversight, and limits on fully automated decision-making in high-impact employment contexts. Without such interventions, algorithmic management risks enabling a form of workplace governance in which control expands while legal accountability recedes.