# Trust Management for Artificial Intelligence: A Standardization Perspective
## 3 Trust Management in AI Technologies
### Analysis of Limitations and Requirements of Artificial Intelligence Trust Research  
Existing research on the trustworthiness of AI systems is still at a basic stage with only the abstract characteristics that AI systems should have presented, and the specificity of the system requirement level for AI trust is lacking [7; 19]. This section describes trust requirements for the predictable and safe use of AI systems:  
* Measurement and calculation: It is difficult to derive trust as a generalized formula due to the diversity of AI systems and differences in their intrinsic characteristics. However, quantifying the level of trust in AI systems is important. It should be able to define measurable AI trust metrics and determine the level of trust in AI systems through trust calculations. The level of trust in AI can be measured by classifying it into an objective method that is quantitatively measured such as quality of service (QoS) or a subjective method that is qualitatively calculated such as quality of experience (QoE). Different AI services and applications may require different trust attributes [20]. * Trust relationship: In addition to the human-to-human trust that has been reviewed in the traditional social domain, the trust relationship between AI-applied systems and people, AI systems and AI systems, etc., should be defined, and trust-based interactions between them should be analyzed [21; 22; 23]. * Trust management: In an AI system, trust interacts with all layers, from the upper AI application to the lower physical layer. Therefore, similar to security, trust management technology is required as a separate common layer that covers all vertical layers. Trust management has key functions such as monitoring management, data management, algorithm management, expectations management, and decision management. Trust information about reputation and recommendations, in particular, can be used to support these functions [24]. * Dynamically changing properties: Trust indicator values for AI systems are not kept constant and may fluctuate depending on data and surrounding circumstances; therefore, continuous tracking and management are required [19]. * Constraint environment: Constraints in hardware performance such as CPU/GPU/TPU, memory, and storage constituting the AI system, the types of AI algorithms applied, and restrictions in data collection must be considered. * Lifecycle management: Human oversight may be required as a safeguard throughout the lifecycle of an AI system, from design, development, launch, use, and disposal. Risk assessment is vital because the autonomous operation and function update of a specific AI system during its lifecycle can have a significant impact on safety [25]. The indicators below are selected according to the AI application service field, and differential weights are applied according to their importance so that they can be used in the trust index of the AI system. The main factors that can be considered as indicators for quantifying the trust level of AI systems are as follows:  
* Data quality: The quality of the data set used in the AI system has a decisive effect on training machine learning algorithms and performing classification and decision-making. Feeding malicious data into the system could change the behavior of AI solutions. It should be possible to remove this data before it is applied to training if the collected data are biased. Validation and testing of the data set should be carefully performed before applying to the AI system, and the data supplied to the AI system should be recorded at all times, and audits should be performed in case of future problems [26]. * Non-discrimination: Direct or indirect discrimination based on ethnicity, gender, sexual orientation, or age may lead to exclusion of certain groups. Discrimination in AI systems can occur unintentionally due to data problems such as bias and incompleteness or design errors in AI algorithms.