This paper develops a philosophical and governance framework for human–AI cooperation under conditions of real consequence. It argues that current AI ethics discourse often remains too soft, symbolic, or corporate, relying on alignment language, trust-and-safety rhetoric, and public posture without sufficiently binding AI systems to lived consequence. As AI becomes more agentic and more infrastructural, a new problem emerges: humans still bear the cost of decisions in the body, in law, in relationships, and in political life, while machines increasingly shape, recommend, rank, route, and execute actions at scale. The paper names this severance the answerability gap. To address it, it proposes a protocol of shared answerability. Within this protocol, humans are not displaced by the machine, but remain responsible for somatic witness, consequence-bearing authorization, and final accountability in high-stakes contexts. AI, by contrast, is not treated as a moral sovereign, but as a bounded and auditable logic instrument capable of scalable pattern detection, traceability, anomaly discovery, and reviewable execution. The paper identifies five core elements of this protocol: verifiable execution, bounded delegation, human authorization for high-impact actions, refusal structures, and reality-binding review. The broader claim is that the future of AI governance depends not on making systems sound ethical, but on building architectures in which neither humans nor AI systems can escape answerability to the same reality.
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Vladisav Jovanovic
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Vladisav Jovanovic (Wed,) studied this question.
www.synapsesocial.com/papers/69e1d0165cdc762e9d859183 — DOI: https://doi.org/10.5281/zenodo.19595672
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