# Trust Management for Artificial Intelligence: A Standardization Perspective
## 1 Introduction  
A large amount of data are being generated and accumulated in various service fields, and artificial intelligence (AI) technology, which has the ability to extract meaningful information and make decisions by analyzing it, is rapidly developing and spreading [1; 2; 3].  
AI systems build a model framework by installing software and libraries based on the hardware infrastructure, and then preprocess, analyze, and visualize the input data [4]. Developing and operating these AI systems require sophisticated and complex processes which are very difficult to implement. There is continuous research and development to significantly reduce the complexity and operation time while increasing the accuracy of AI systems [5]. In addition, there are many research activities to emphasize trust for giving confidence concerning the predictions processed by AI algorithms in a system [6; 7; 8].  
In discussing the characteristics of AI technology, classification according to Weak AI and Strong AI is generally used. Weak AI is an artificial intelligence technology that focuses on relatively narrow areas such as image recognition, voice recognition, and data classification, while Strong AI is an artificial intelligence technology that enables machines to intelligently solve various problems with true intelligence and self-recognition [9].  
Weak AI is already widely applied and yields remarkable results; however, there are still many controversies relating to the technical potential and social impact of Strong AI.  
AI-based systems and applications can sometimes behave in ways the system designer did not foresee. A recent study from Stanford University found that an AI-based facial recognition algorithm discriminated against homosexuals and heterosexuals on dating sites with up to 91% accuracy, raising tricky ethical questions [10]. This study identified thatfaces contain much more information about sexual orientation than can be perceived and interpreted by the human brain. As demonstrated in this study, the widespread adoption of AI technologies is strongly required to proactively consider privacy risks with the need for safeguards and regulations to resolve ethical issues such as unintended discrimination.  
The AI system is a black box with very large information asymmetry between developers and users and high uncertainty. Therefore, it is vital to forecast the potential risks of AI systems and to evaluate and manage the trust to increase transparency and accountability. However, due to several reasons, evaluating and managing the reliability of AI systems and algorithms are very complex [9].  
Many software and hardware components existing in infrastructure, AI models and data processing platforms, applications/services, etc., compose the AI system through complex connections [4]. The reliability of AI systems is largely driven by algorithms and data. Generally, it will be difficult to achieve a reliable and stable AI ecosystem if there is no way to measure, analyze, and verify trust in AI systems.  
This study identifies the requirements for trust management of AI systems to prevent unintended damage caused by malfunctions of AI algorithms and data bias. We propose a trust management framework for AI systems and aim to analyze strategies and issues for international standardization based on these requirements.  
Following this introduction, Section 2 reviews the standardization trends related to AI trust management, Section 3 analyzes the existing research status and limitations related to AI reliability, Section 4 proposes the AI trust management framework, Section 5 analyzes the international standardization strategy and related issues for the AI trust management framework and suggests new standardization items, and Section 6 concludes by summarizing this study and suggesting future research.