Constitutive vs. Corrective: A Causal Taxonomy of Human Runtime Involvement in AI Systems
Kevin Baum, Johann Laux

TL;DR
This paper clarifies the ambiguous terminology of human involvement in AI systems by proposing a causal taxonomy distinguishing constitutive and corrective roles, with implications for design, regulation, and interdisciplinary understanding.
Contribution
It introduces a causal, structural taxonomy of human involvement in AI, differentiating HITL and HOTL based on causal roles, temporal modes, and cognitive integration, enhancing clarity and regulatory guidance.
Findings
HITL is constitutive, requiring human contribution for decisions.
HOTL is corrective, capable of preventing or modifying outputs.
Normative oversight demands genuine intervention capacity.
Abstract
As AI systems increasingly permeate high-stakes decision-making, the terminology regarding human involvement - Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversight - has become vexingly ambiguous. This ambiguity complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and can lead to regulatory uncertainty. We propose a clarification grounded in causal structure, focused on human involvement during the runtime of AI systems. The distinction between HITL and HOTL, we argue, is not primarily spatial but causal: 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 three temporal modes - synchronous, asynchronous, and anticipatory - situated within a…
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Taxonomy
TopicsEthics and Social Impacts of AI · Embodied and Extended Cognition · Human-Automation Interaction and Safety
