# Trust Management for Artificial Intelligence: A Standardization Perspective
## 3 Trust Management in AI Technologies  
This section first analyzes the basic configuration and operation procedure of the artificial intelligence system and describes the status of trust management technology related to AI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Standard Body** & **Standard Group** & **Standard Document** & **Main Content** \\ \hline \multirow{6}{*}{ITU–T} & & Y.3051 & Basic principles for a trusted environment in ICT infrastructure. \\ \cline{2-3}  & & Y.3052 & Trust provisioning overview for ICT infrastructure and services. \\ \cline{2-3}  & & Y.3053 & Trust networking framework with trust-centric network domains. \\ \cline{2-3}  & & Y.3054 & Trust-based media services framework. \\ \cline{2-3}  & & Y.3057 & Trust index for ICT infrastructure and services. \\ \cline{2-3}  & & Y.trust-arch & Functional architecture for trust-based service provisioning. \\ \cline{2-3}  & & Y.3056 & An open bootstrap framework that supports trust networking and services for distributed ecosystems. \\ \cline{2-3}  & & Y.3055 & Trust-based personal data management platform framework. \\ \cline{2-3}  & & FG–DPM & TR D4.3 & Technical enabler overview for trust data. \\ \cline{2-3}  & & SG17 & X.5 GSec-t & Trust relationship-based security framework in the 5G ecosystem. \\ \hline \multirow{6}{*}{ISO/IEC} & & TR 24028 & Artificial intelligence trust overview. \\ \cline{2-3}  & & TR 24368 & Artificial intelligence ethics and social importance concept. \\ \cline{2-3}  & & CD 23894 & Artificial intelligence—risk management \\ \cline{2-3}  & & TR 24027 & AI systems and AI-based decision-making bias. \\ \cline{2-3}  & & TR 5254 & Goals and methods for exploitability of ML models and artificial intelligence systems. \\ \hline \hline \end{tabular}
\end{table}
Table 1: International standardization trends related to artificial intelligence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Standard Body** & **Standard Group** & **Standard Document** & **Main Content** \\ \hline \multirow{6}{*}{ITU–T} & & Y.3172 & An architectural framework for the application of machine learning in future networks including IMT-2020. \\ \cline{2-3}  & & Y.3173 & A method for measuring the intelligence level of future networks including IMT-2020. \\ \cline{2-3}  & & Y.3170 & Data processing framework to apply machine learning to future networks including IMT-2020. \\ \cline{2-3}  & & Supplement 55 to Y.3170-series & Machine Learning Use Cases in Future Networks such as IM-2020. \\ \cline{2-3}  & & Y.ML-IMT2020-NA-RAFR & AI-based resource control and failure recovery automation in future networks including IMT-2020. \\ \cline{2-3}  & & Y.ML-IMT2020-serv-prov & AI-based user-driven network service provisioning in future networks including IMT-2020. \\ \cline{2-3}  & & Y.ML-IMT2020-MP & Machine Learning Marketplace in Future Networks including IMT-2020 \\ \cline{2-3}  & & Y.IMT2020-AIICDN-arch & AI integrated cross-domain network structure in future networks including IMT-2020. \\ \hline \multirow{6}{*}{SG16} & & Y.Sup.AI4 IoT & AI role in IoT data management and implementation of AI-based technology for smart cities \\ \cline{2-3}  & & Y.4472 & An open data application programming interface for IoT data in smart cities. \\ \cline{2-3}  & & FG–AI4H Whitepaper & White Paper for the ITU/WHO Focus Group on AI Health. \\ \hline \hline \end{tabular}
\end{table}
Table 1: International standardization trends related to artificial intelligence.