arXiv:2606.00947v1 Announce Type: new Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing regulatory requirements for post-market monitoring. We argue that this convergence creates a distinct and under-recognized class of trustworthiness failures, which we term "Silent Failures." These include amplified bias, fairness collapse, and alignment erosion that may remain difficult to detect because federated learning's privacy constraints limit visibility into model behavior. A landscape analys
The increasing deployment of personalized foundation models through federated learning, coupled with growing regulatory requirements for post-market monitoring, makes understanding these failure modes critical now.
This identifies a critical, under-recognized class of trust and safety failures in AI, which could undermine public confidence and regulatory compliance for increasingly central AI systems.
The focus shifts from general AI trustworthiness to specific 'Silent Failures' inherent in federated personalization, prompting new detection and mitigation strategies.
- · AI ethics and safety researchers
- · Auditing and monitoring solution providers
- · Responsible AI developers
- · Companies deploying unmonitored federated personalization
- · Users affected by unaddressed bias and fairness issues
- · AI developers focused solely on performance metrics
Companies will increase investment in monitoring and explainability tools for federated AI systems.
New regulatory frameworks may emerge, specifically addressing 'Silent Failures' in personalized foundation models.
Public distrust in AI could grow if these failures are not proactively addressed, hindering broader adoption of personalized AI.
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Read at arXiv cs.LG