Lifespan aware evaluation improves AI safety for all ages
The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI
Computers and SocietyArtificial IntelligenceHuman-Computer Interaction
Summary
AI systems are usually tested assuming users are adults, but this misses risks to children and older adults. The authors studied harms involving AI across age groups and found that risks are highest for the very young and the very old. They created a matrix to understand vulnerabilities across life stages and proposed new evaluation steps for AI that consider these differences. This approach aims to help ensure AI is safer for people of all ages.
What this means in practice
- •For ai safety teams: Design evaluation processes that consider user vulnerabilities across all life stages to better identify and mitigate age-specific AI risks.
- •For healthcare technology regulators: Implement lifespan-calibrated evaluation frameworks to monitor AI systems in care settings, reducing harm to children and elderly patients.
A position paper. It proposes an approach and reports no results.
Authors
Casey M. Williams, Jennifer Victoria Scurrell
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.