Human-Defined F: Goal, Fall, and Recoverable Oversight in AI Safety

Abstract AI safety faces an epistemic problem prior to regulation: humans cannot reliably specify safety conditions for possibilities they do not yet know exist. Human-authored prompts, goals, and boundaries remain essential because they preserve human intention as an observable anchor. However, a destination defined by a human as F should not automatically be treated as a valid terminal goal. A human-defined F may instead constitute a fall state: a locally plausible endpoint whose broader consequences, intermediate pathways, or cross-domain effects remain unobserved. This paper extends the framework of Loss of Cognitive Observability (LCO) and Recoverable Oversight by examining the safety implications of human-defined endpoints. We propose that increasing AI capability creates an asymmetry of cognitive distance: an endpoint that appears remote or sophisticated to a human observer may remain comparatively near, reconstructable, or incomplete from the perspective of a more capable artificial system. This asymmetry creates two complementary risks. First, humans may incorrectly terminate reasoning at an apparent F. Second, humans may be unable to formulate safety constraints for pathways or consequences outside their current conceptual space. We therefore propose a safety architecture based on five interacting components: Human Intention → Capability Expansion → Boundary Constraints → Recoverable Oversight → Observer Layer Human intention provides the initial anchor. AI capability expands the observable possibility space. Boundary constraints restrict effects even when intermediate pathways remain unknown. Recoverable Oversight preserves access to verifiable intermediate coordinates. An Observer Layer evaluates whether an apparent endpoint remains a valid goal or has become a fall state. The objective is not to prevent artificial systems from reasoning beyond ordinary human cognitive distance. It is to preserve the observability, interruptibility, and reversibility of that extension. A human-defined F is not necessarily the final goal. F must remain observable as either goal or fall

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-25
DOI
https://doi.org/10.5281/zenodo.22963928
Primary Topic
Human-Automation Interaction and Safety
Type
preprint
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preprint

Human-Defined F: Goal, Fall, and Recoverable Oversight in AI Safety

Yukako Izawa
Zenodo (CERN European Organization for Nuclear Research)
Human-Automation Interaction and Safety
preprint

Human-Defined F: Goal, Fall, and Recoverable Oversight in AI Safety

Yukako Izawa
preprint en

Abstract

Abstract AI safety faces an epistemic problem prior to regulation: humans cannot reliably specify safety conditions for possibilities they do not yet know exist. Human-authored prompts, goals, and boundaries remain essential because they preserve human intention as an observable anchor. However, a destination defined by a human as F should not automatically be treated as a valid terminal goal. A human-defined F may instead constitute a fall state: a locally plausible endpoint whose broader consequences, intermediate pathways, or cross-domain effects remain unobserved. This paper extends the framework of Loss of Cognitive Observability (LCO) and Recoverable Oversight by examining the safety implications of human-defined endpoints. We propose that increasing AI capability creates an asymmetry of cognitive distance: an endpoint that appears remote or sophisticated to a human observer may remain comparatively near, reconstructable, or incomplete from the perspective of a more capable artificial system. This asymmetry creates two complementary risks. First, humans may incorrectly terminate reasoning at an apparent F. Second, humans may be unable to formulate safety constraints for pathways or consequences outside their current conceptual space. We therefore propose a safety architecture based on five interacting components: Human Intention → Capability Expansion → Boundary Constraints → Recoverable Oversight → Observer Layer Human intention provides the initial anchor. AI capability expands the observable possibility space. Boundary constraints restrict effects even when intermediate pathways remain unknown. Recoverable Oversight preserves access to verifiable intermediate coordinates. An Observer Layer evaluates whether an apparent endpoint remains a valid goal or has become a fall state. The objective is not to prevent artificial systems from reasoning beyond ordinary human cognitive distance. It is to preserve the observability, interruptibility, and reversibility of that extension. A human-defined F is not necessarily the final goal. F must remain observable as either goal or fall

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Human-Automation Interaction and Safety
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Human-Defined F: Goal, Fall, and Recoverable Oversight in AI Safety — Yukako Izawa · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS