arXiv (All)
open access
PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic
Trustworthiness has become a key requirement for deploying artificial intelligence in safety-critical applications, yet conventional metrics such as accuracy fail to capture uncertainty or the reliability of predictions, particularly under adversarial or degraded conditions. This paper introduces th
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CRiDiT: Instantiating a run-time testbed for trust calibration in AI-infused systems#1051232
- Proceedings of the Workshop on Artificial Intelligence Safety 2020 co-located with the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan, January, 2021.#489558
- MODELING TRUST IN ARTIFICIAL INTELLIGENCE FOR NAVAL COMMAND AND CONTROL#663856
- A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions#964503
- Ahimsa AI Framework: A Multi-Layer Approach to Implementing Non-Violence Principles in Large Language Model Safety#74876
Record · ID 1199722
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive