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AI-based technology has achieved many great things, such as facial recognition, medical diagnosis, and selfdriving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data security, data privacy, and ethical problems of AI-based technology also pose significant risks for users, developers, and governments. As the AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. This study will focus on the ethics and morality issues that may be caused by AI, and may arise because of AI. This research uses a qualitative approach and will conduct interviews with AI experts, programmers, workers, labor unions' representatives, legislators, and other stakeholders. The research focuses on two research questions: What are the perceived ethical and moral issues with AI, and how can these issues be solved or attenuated. [ABSTRACT FROM AUTHOR]

COVID-19 is sweeping across the globe. Some countries are collecting anonymized data to study the movement of people and others are providing more detailed information about individuals in addition to the movement data. Surveillance tools are widely used to stem the spread of the COVID-19. South Korea uses smartphone location data, surveillance camera footage, and credit card data to create a publicly available map that tracks the paths of COVID-19 patients. Austria's network operators share anonymized location data with the government, but the data may remain at risk from re-identification in the future. The Polish government releases an app for people in quarantine, requiring them to upload geo-located selfies periodically. In some countries, police and military personnel are also involved in enforcing quarantines, curfews, and social distancing, which may encroach on personal freedom and privacy. There is no doubt that the use of technologies, such as geolocation and facial recognition, can help to slow and manage the spread of COVID-19 and enforce social distancing, but people are concerned about the expanded and potentially questionable uses of technologies that may result in privacy issues (Siau & Wang, 2020). Many questions remain to be answered. How the data is being shared? How to deal with the data and the surveillance capabilities once the pandemic subsides? How to prevent unauthorized individuals or institutions from gaining access to the data? Privacy is recognized as a fundamental human right, essential for freedom, democracy, and psychological well-being. As technology developed, especially the progress of mobile devices (Siau & Shen, 2002, 2006), concerns about privacy (location data in particular) increase (Shokri et al. 2011). Some organizations are dedicated to protecting individual privacy while sharing information, and encrypting the data collected to prevent a hacker from accessing identifiable information. However, as evidenced by data breaches, information is increasingly exposed to hacking, which results in information security and privacy issues (Siau & Wang, 2018; Wang & Siau, 2019). This qualitative study will examine how organizations and authorities are dealing with the tradeoff between protecting individual privacy and saving lives in the pandemic. In the first phase of the study, data security and privacy officers in various government and healthcare organizations will be interviewed. In the second phase, the findings from the first phase will be used to design questionnaires, which will be administered in government organizations. This will be a multi-country and multi-culture research. [ABSTRACT FROM AUTHOR]

A health chatbot is an artificial intelligence program that can conduct an intelligent conversation via auditory or textual methods regarding healthcare issues. An estimate by Market research firm Grand View Research is that the global chatbot market will reach $1.23 billion by 2025. Millions of chatbots have been sold to hospitals. Many health chatbots can be downloaded for free. These health chatbots provide a variety of services. For instance, Babylon Health allows users to report symptoms of their illnesses, checks them against a database of diseases, and offers appropriate courses of action. If needed, the chatbots will offer the users a live video consultation with a real doctor. Florence, a health chatbot, can remind a patient to take his/her pills, track the user's health, and find the nearest pharmacy and doctor's office if needed. The capabilities of health chatbots have been growing steadily. With the aging population and rising healthcare costs, health chatbots will likely become the first point of contact for primary care. Health chatbots are not making decisions per se but offer users rational options. Physicians must trust health chatbots to confidently use them as supporting tools. Patients must trust health chatbots if they are to have faith in the chatbots' diagnoses and recommendations. In the context of health chatbots, trust can be defined as the willingness of users to provide confidential information, accept the recommendations, and follow the suggestions. Reliability, transparency, and explainability affect the trust-building process (Siau and Wang 2018). The chatbots need to be consistent in their recommendations and suggestions. To facilitate trust, the chatbot's "black box" must be opened up. In other words, users need to understand how the recommendations were derived and what information contributed to the recommendations. In addition, the chatbots' algorithms rely heavily on data. The integrity, accuracy, privacy, and security of the data are essential. Otherwise, users will not provide sensitive healthcare information to use the health chatbots. Governance and ethical standards are still in the budding stage for artificial intelligence as a whole (Wang and Siau 2018) and these fields need to be further developed to facilitate the development of trust and adoption of health chatbots. Trust is dynamic -- involving initial trust building and continuous trust development. Many factors affect trust building. Previous literature has shown that personality, institution, cognition, knowledge, and calculative factors have an impact on initial and continuous trust (Wang and Siau 2018). Positive experience affects continuous trust. Are these the key factors that affect trust building in health chatbots? Are there other factors that are important in trust building in health chatbots because of the involvement of artificial intelligence and the health risks with wrong diagnoses? How to improve users' trust and acceptance of health chatbots? The research studies these research questions and develops a trust model that depicts the trust-building process between users and health chatbots. To develop a trust model, research models by Gefen et al. (2003), Bansal et al. (2015), and Siau andWang (2018) will be referenced. A case study involving experts fromthe healthcare industry, patients, and academicswill be conducted. [ABSTRACT FROM AUTHOR]