The visiting scholar of Taiwan Institute for Governance and Communication Research (TIGCR) -- Professor Justin Bullock, Assistant Professor of Public Service and Administration Department, Texas A&M University delivered a public speech on “Creating Artificial Bureaucrat” on July 31st, following the topic “The Impacts of AI (Artificial Intelligence) on Government Policies”. Professor Justin Bullock focuses on how to apply AI to the bureaucratic system and its future development. The speech is hosted by Professor Zhou-Peng, Liao from the Department of Public Administration at National Open University who is also the Deputy Director in Taiwan E-Governance Research Center.
Professor Liao chaired the speech
(Photo credit: TIGCR)
Based on the last speech focusing on applying AI to assisting government discretion, Professor Bullock deeply analyzes the complicated AI system. Learning from Comprehensive AI Systems (CAIS) launched by Drexler, Professor Bullock offers suggestions to the current research of public administration and its future.
Professor first mentions the impacts of fast-developing technology on the development of AI. Due to the assistance of AI, public administration, including decision-making and discretion becomes more flexible and efficient. In the long-term development, the reason why AI can surpass Information Communication Technology which helped officials before rests on its keep-learning ability that can transcend human’s limited storage systems, process data more efficiently and continue to improve the decision-making behaviors according to its learning experiences.
Professor Bullock's speech video
(Video credit: TIGCR)
Professor Bullock delivered a public speech
(Photo credit: TIGCR)
He then refers that for many public servants, there are three functions of AI they care the most. Firstly, systematically building pictures, words or data to help AI learn thereafter. Secondly, using the identification mode of big data to analyze or predict whether the input data can become trends. Thirdly, whether the current human resources can be replaced in the automatic way. According to Professor Bullock, although the learning capability of AI can help it handle data in the efficient and systematic way, there are many factors we need to consider when we create artificial bureaucracy.
Professor Bullock points out that AI owns strong problem-solving capability and can learn new techniques as well as complete missions through complex scientific structures, but as far as we know, disparity exists in understanding the ability between AI and human. Humans can understand their own behaviors deeply, while AI cannot achieve it. When deciding the civil service system should use AI or artificial methods to make decisions, Professor Bullock thinks it depends on whether the official business requires the administrator to have the understanding ability.
Professor Bullock answered the question
(Photo credit: TIGCR)
Because of the bureaucratic characteristic of AI system, Professor Bullock provided advices for current artificial bureaucracy, based on Comprehension AI service (CAIS) given by Drexler (2019). The intelligence of CAIS has improved. It featured on mission-centered characteristic different from the previous AI. When introducing the model in the future, we should decide the assignment of missions by considering the risks and complexity of missions thoroughly.
Professor Bullock answered the question
(Photo credit: TIGCR)
Professor Bullock concluded that AI has become a measurement tool for every field gradually. We should conceive the AI strategy carefully. Like other systems, AI is composed of complicated bureaucratic structure. We can refer to Drexler’s structure to improve the subsystems.
After the speech, in response to the invitation of Professor Chi Huang, Director of TIGCR, Professor Bullock agreed to become one of our members. Hopefully, we can have closer cooperation and interaction in the future. With the consent of Professor Bullock, this speech is fully recorded. Everyone is welcome to review his speech on our official website.
Group photo after the speech
(Photo credit: TIGCR)
