The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI

arXiv:2610.35725v1 Announce Type: new Abstract: The gap between AI capability deployment and safety assurance is structural and cannot be closed through internal evaluations alone. Analyzing documented harms (2017-2026) across the human lifespan reveals that disparate incidents --- from voice assistants endangering children to AI companions implicated in adolescent suicide and automated care denials for the elderly --- reflect a single failure: evaluating systems against an implicit adult user model. We introduce five developmental moderators structured into a Lifespan Vulnerability Matrix across six life stages. The matrix reveals a nonmonotonic risk distribution: vulnerability peaks at both ends of the lifespan, governance coverage is asymmetrical, and authority to convert model errors into real-world impact is scrutinized least. We then propose a set of lifespan design principles generalizing existing child-protective instruments, and an institution-agnostic, three-tier external evaluation framework --- comprising automated simulation, expert red teaming, and post-deployment monitoring --- indexed directly to matrix risk cells.

The text highlights a structural gap between AI capability and safety, revealing disparate harms across the human lifespan.

The article identifies serious risks from AI systems due to inadequate evaluation for vulnerable age groups, particularly children and the elderly, revealing real harms that affect human flourishing. However, it proposes a systematic, multi-tiered framework for improving AI safety externally and across the lifespan, which can materially mitigate these harms and promote safer deployment. The balance of risks and mitigations places it moderately optimistic overall.

This is a proposed academic framework citing real documented harms (child endangerment, adolescent suicide linked to AI companions, elder care denials) but presents them as background motivation rather than new primary evidence. The implication is concerning but the paper itself is a governance proposal, not a new harm disclosure.

The paper identifies a structural problem where AI safety evaluations fail to account for vulnerabilities across the human lifespan, leading to documented harms. It proposes a framework to address this, but the core implication is that current AI development is inherently unsafe for vulnerable populations. The claim of harms occurring from 2017-2026 suggests this is an ongoing, rather than future, problem.

The paper documents specific harms to children, adolescents, and the elderly caused by AI systems. It argues that current evaluation methods are inadequate because they assume an adult user model. The proposed Lifespan Vulnerability Matrix and evaluation framework aim to address these gaps, but the need for such measures implies significant existing risks.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   7d6b5007-72b8-45fe-8bde-2ee66450d473
           https://arxiv.org/abs/2610.35725
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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      -50  (Adverse)
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CONFIDENCE 63%