Conference Agenda (All times are shown in Eastern Daylight Time)

The open source of DeepSeek has enabled the application of artificial intelligence generated content (AIGC) to enter a rapid development stage. However, it has also increasingly highlighted many problems. This study delves into the specific problems of AIGC, constructs a collaborative governance mechanism from three aspects: goal collaboration, process collaboration, and inter-subject collaboration, and provides an implementation path for multi-subject collaborative governance. The study found that in the DeepSeek era, AIGC technology faces problems such as user privacy leakage, insufficient content quality assessment, and intellectual property and ethical conflicts during its application. This paper emphasizes that establishing a collaborative governance mechanism is a key way to deal with these problems in the long term. The government, industry, platform, and users should participate together to strengthen industry supervision, improve the self-discipline review mechanism, and enhance AI literacy education, so as to jointly promote the long-term stability and healthy development of the artificial intelligence industry. This study is of great significance to ensuring information security and promoting the healthy development of the artificial intelligence industry.

Government agencies' use of AI has the capacity to improve and radically transform the essence of digital government. It is critical to avoid restrictive policies that constrain innovation or, conversely, insufficient regulation that could lead to social and ethical issues. However, the rapid deployment of AI in government has faced criticism for posing significant challenges to democracy, especially given the inherent governance issues. It is critical as it encompasses responsible and effective use to protect democratic values in government agencies’ structures, processes, and practices. This study contributes to information science by exploring how federal agencies, in their compliance plans, are responding to the Management and Budget memorandum requirement for advancing AI governance, innovation, and risk management through a content analysis of 23 federal agencies’ compliance plans. The conclusion indicated that government agencies predominantly advance AI governance, innovation, and risk management by focusing on control rather than transformative innovation.

AI and data are mutually influential, with AI outputs shaped by training data and data often generated, processed, and categorized by AI. The use of both AI and data by government organizations is guided by policy documents; existing research has explored data policies or AI policies but has rarely put both in conversation, despite their linked subject matter. We adopt a mixed-methods approach to analyze the data and AI strategies of the Government of Canada, investigating whether the data-AI relationship is reflected in policy documents. Our findings demonstrate a disconnect between Canadian data and AI policies, illustrate potential implications of this disconnect, and contribute to ASIS&T 2025 conversations about the necessity of information science for the responsible, ethical use of data and AI in government settings.