Anticipatory Human Oversight of Agentic AI: A Philosophical Account

arXiv:2609.24242v1 Announce Type: new Abstract: Human oversight is widely held to mitigate the risks of AI systems. Even for systems that produce discrete outputs at identifiable decision points, the realisation of human oversight as a reactive measure is empirically fragile, yet increasingly well understood. However, for agentic AI -- systems that plan, decompose goals, and execute multi-step actions over extended horizons -- reactive oversight reaches its structural limits: intervention on individual actions defeats the autonomy that motivates the deployment, while intervention on aggregate patterns is too coarse for harms whose cumulative consequences only become legible after the fact. This paper argues that reactive oversight must be complemented by an anticipatory mode: oversight exercised before the agent acts, by specifying the normative agenda that structures the space of permissible action and refining it iteratively through specification, runtime, and inspection. The two are complements -- the agenda's escalation conditions specify when reactive intervention is invoked. Drawing on Meaningful Human Control, we read anticipatory oversight as the operationalisation of distal-reason tracking. In addition, we argue that the proposed framework yields a specific responsibility architecture by design: occupying the anticipatory mode is the discharge of a role-grounded prospective obligation, and backward-looking responsibility takes the form of strict moral answerability -- rationalistic, relational, and holding regardless of fault, in virtue of the principal's prior opp

Proposes anticipatory oversight for agentic AI

The paper identifies structural limits of reactive human oversight on agentic AI and proposes anticipatory oversight to improve normative control and responsibility. This approach could reduce risks by clarifying moral responsibility and enabling proactive measures. However, practical effectiveness and implementation challenges leave moderate uncertainty and limited immediate societal impact.

This is a theoretical/philosophical contribution about oversight frameworks, not an empirical finding about deployed systems or measured harms. It implies real structural challenges in governing agentic AI but offers conceptual solutions rather than evidence of outcomes, so its societal implication is modest and uncertain.

The paper identifies fundamental limitations of reactive human oversight for agentic AI, which is a concern regarding human control. It proposes an 'anticipatory oversight' framework to address this, suggesting a potential improvement in managing AI risks. The implication for human flourishing is moderately negative due to the identified limitations.

The paper proposes a framework for anticipatory oversight of agentic AI. This could enhance safety and ethical alignment. It also suggests a clear responsibility architecture, which is positive for accountability.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   4bc93634-2f13-4415-a12a-d18680e2a11c
           https://arxiv.org/abs/2609.24242
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -2  (Mixed or uncertain)
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CONFIDENCE 75%