# A Maturity Model for Trustworthy AI Software Development
## 1 Introduction  
Along with big data and the Internet-of-Things (IoT), artificial intelligence (AI) has become a key technology of the fourth industrial revolution and is widely used in various industry areas, including finance, medical/healthcare, robotics, autonomous driving, smart factories, shopping, delivery, and so on. AI100, an ongoing study conducted by a group of AI experts in various fields, focuses on the influence of AI. It has been predicted that AI will totally change human life by 2030 [1]. In addition, according to a report by McKinsey, about 50% of companies use AI in their products or services, and 22% of companies increased their profits by using AI in 2020 [2]. Furthermore, IDC, a U.S. market research and consulting company, predicts that the global AI market will grow rapidly by 17% annually from $327 billion in 2021 to $554 billion in 2024 [3]. Unlike traditional software that operates according to rules defined and implemented by humans, the operation of AI software is determined by data used for training. In other words, AI software can make different decisions in the same situation when the data is changed, even though the core architecture of the AI software is the same. Due to the fact that AI software relies on training data, various problems that developers did not expect are occurring. For example, unintended bias in the training data can exacerbate existing ethical issues, such as gender, race, age, and/or regional discrimination. Insufficient training data can cause safety problems, such as autonomous driving accidents. Additionally, the Harvard Business Review warned that these kinds of problems could devalue companies' brands [4]. The following provides some examples of actual AI-software-related problems that have drawn global attention. * [PERSON], a chatbot developed by Microsoft, was trained using abusive language by far-right users. As a result, [PERSON] generated messages filled with racial and gender discrimination; it was shut down after only 16 h [5]. * Amazon developed an AI-based resume evaluation system, but it was stopped due to a bias problem that excluded resumes including the word \"women's\". This issue occurred because the AI software was trained using recruitment data collected over the past 10 years where most of the applicants were men [6]. * The AI software in Apple cards suggested lower credit limits for women as compared to their husbands, even though they share all assets, because it was trained by male-oriented data [7]. * Google's image recognition algorithm misrecognized a thermometer as a gun when a white hand holding a thermometer was changed to a black hand [8]. * Tesla's auto pilot could not distinguish between a bright sky and a white truck, causing driving accidents [9]. To address social issues exacerbated by AI software, several governments, companies, and international organizations have begun to publicly share principles for developing and using trustworthy AI software over the past few years. For example, Google proposed seven principles for AI ethics, including preventing unfair bias [10]. Microsoft also proposed six principles of responsible AI, including fairness and reliability [11]. Recently, Gartner reported the top 10 strategic technology trends for 2023, and one of the trends is AI Trust, Risk, and Security Management (AI TRISM) [12]. In addition, the need for international standards about trustworthy AI is being discussed. Specifically, the EU announced an AI regulation proposal for trustworthy AI [13]. The EU also classified AI risk into different categories, including unacceptable risk, high risk, and low or minimal risk, and argued that risk management is required in proportion to the risk level [14]. In this paper, we propose the AI Maturity Model (AI-MM), a maturity model for trustworthy AI software to prevent AI-related social issues.