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

Recent research from arXiv highlights the ambiguity surrounding terminology for human involvement in AI systems, specifically 'Human-in-the-Loop' (HITL) and 'Human-on-the-Loop' (HOTL). The paper proposes a causal taxonomy to clarify these terms, defining HITL as constitutive (human contribution is necessary for decision output) and HOTL as corrective (human intervention is external to the primary causal chain but can modify or prevent outputs). This clarification aims to improve interdisciplinary collaboration and reduce regulatory uncertainty.

The precise understanding and consistent application of terminology for human involvement in AI are critical for effective governance and responsible development of AI technologies. This clarity can mitigate risks associated with misinterpreting human-AI interaction models, ensuring accountability and ethical considerations are properly embedded in high-stakes decision-making systems.

Existing terminology for human involvement in AI (HITL, HOTL, Human Oversight) is ambiguous.

Forward consideration, not a verified fact.