A multilevel framework for AI governance

To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued guidelines and ethical frameworks for trustworthy AI, without a mediating governance structure, these ethical principles will not translate into practice. In this paper, we propose a multilevel governance approach that involves three groups of interdependent stakeholders: governments, corporations, and citizens. We examine their interrelationships through dimensions of trust, such as competence, integrity, and benevolence. The levels of governance combined with the dimensions of trust in AI provide practical insights that can be used to further enhance user experiences and inform public policy related to AI.Comment: This paper has been accepted for publication and is forthcoming in The Global and Digital Governance Handbook. Cite as: Choung, H., David, P., & Seberger, J.S. (2023). A multilevel framework for AI governance. The Global and Digital Governance Handbook. Routledge, Taylor & Francis Grou

sj-docx-1-pus-10.1177_09636625231224592 – Supplemental material for Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics

Supplemental material, sj-docx-1-pus-10.1177_09636625231224592 for Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics by Prabu David, Hyesun Choung and John S. Seberger in Public Understanding of Science</p

sj-docx-1-pus-10.1177_09636625241228733 – Supplemental material for Communicating trust and trustworthiness through scientists’ biographies: Benevolence beliefs

Supplemental material, sj-docx-1-pus-10.1177_09636625241228733 for Communicating trust and trustworthiness through scientists’ biographies: Benevolence beliefs by Samantha Hautea, John C. Besley and Hyesun Choung in Public Understanding of Science</p

Voter Typology Cluster Analysis

Despite changes in the U.S. political landscape, many scholars still rely on the traditional left-right binary to understand voters. Such a reductive typology obfuscates voter differences, particularly regarding subgroups within political parties, as evident by 2016 U.S. Presidential election. This data comes from a survey distributed right before November 8, 2016 and includes which includes items about 17 enduring political values and worldviews. Studies using this dataset apply clustering techniques to typologize voters, both holistically and as part of specific groups (e.g., Independents). Results from this data emphasis the need to re-consider how best to communicate with different clusters of the American electorate