arXiv:2511.20586v4 Announce Type: replace-cross Abstract: Trustworthiness has become a key requirement for the deployment of artificial intelligence systems in safety-critical applications. Conventional evaluation metrics, such as accuracy and precision, fail to appropriately capture uncertainty or the reliability of model predictions, particularly under adversarial or degraded conditions. This paper introduces the Parallel Trust Assessment System (PaTAS), a framework for modeling and propagating trust in neural networks using Subjective Logic (SL). PaTAS operates in parallel with standard neu
The increasing deployment of AI in safety-critical applications necessitates more robust trust and reliability mechanisms, which conventional metrics fail to provide adequately.
This framework addresses a fundamental limitation of current AI systems regarding uncertainty and reliability, critical for widespread adoption in sensitive domains and for regulatory acceptance.
The ability to quantify and propagate trust within neural networks introduces a new paradigm for AI evaluation, moving beyond simple accuracy to include reliability under adverse conditions.
- · AI developers in safety-critical domains
- · Regulatory bodies developing AI standards
- · Industries like autonomous vehicles and healthcare AI
- · Organizations prioritizing AI explainability and trust
- · AI systems relying solely on traditional performance metrics
- · Developers ignoring trust and reliability in critical applications
Increased trustworthiness of AI models, leading to greater adoption in risk-averse sectors.
New regulatory frameworks and certification processes for AI systems will emerge, requiring such trust metrics.
The development of a 'trust layer' for all AI, fostering public confidence and mitigating existential risks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG