Constitutive vs. Corrective: A Causal Taxonomy of Human Runtime Involvement in AI Systems

Abstract

As AI systems permeate high-stakes decision-making, the terminology of human involvement---Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversight---has become vexingly ambiguous. This complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and breeds regulatory uncertainty. We propose a clarification grounded in causal structure, focused on runtime involvement. The distinction between HITL and HOTL is best drawn not spatially---in terms of a human's position "in" or "on" a loop---but causally: HITL is constitutive (a human contribution is necessary for the decision output), while HOTL is corrective (external to the primary causal chain, capable of preventing or modifying outputs). Within HOTL, we distinguish temporal modes---synchronous, asynchronous, and anticipatory---situated in a nested model of provider and deployer runtime. A second, orthogonal dimension captures cognitive integration: whether human and machine form complementary or hybrid intelligence, yielding four distinct configurations. Finally, we separate these descriptive categories from the normative requirements they serve: statutory "Human Oversight" is a normative mode of HOTL demanding not merely a corrective causal position but genuine preparedness and capacity for effective intervention. Because the same person may occupy both roles, this role duality must be treated as a design problem requiring architectural and epistemic mitigation.