# Human Resilience in the AI Era -- What Machines Can't Replace

arXiv: https://arxiv.org/abs/2510.25218 · HTML: https://mlsanity.com/paper/2510.25218/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Biggest Risk of Embodied AI is Governance Lag](https://mlsanity.com/paper/2604.21938/) (2026)
- [Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It](https://mlsanity.com/paper/2606.12442/) (2026)

## Abstract (arXiv)

AI is changing work and decision making faster than many institutions can adapt their operating practices. We argue that this adaptation gap makes human resilience a core capability for the AI era. We define resilience as the capacity to absorb disruption while preserving effective action and human agency around core purposes. The framework operates at three interacting levels. Psychological resilience keeps a person goal-directed under stress. Social resilience makes trusted support and correction available across a group. Organizational resilience turns detected problems into learning and recovery. We connect established resilience and technostress research with direct AI-in-the-loop experiments. General resilience is trainable, while AI-specific causal evidence is still emerging. Direct AI studies show that assistance can raise productivity and spread expertise. Other experiments show improved expressed empathy and more calibrated reliance. We translate these findings into a practical agenda for AI education, workplace design, governance, and evaluation. The central proposal is socio-technical: structural safeguards define the operating boundary, while resilient people and institutions provide adaptive capacity when conditions change.