Keynote - Trust-based Public Policy Platform in the Age of Uber-Intelligence

Public policymaking involves problem identification, data gathering, policy analysis, design, selection, and implementation, followed by evaluation. It involves public engagement in this process to understand the diverse opinions from different stakeholders and impacts. The public engagement is mediated by Information Technology that can support deliberations by providing data organization, data analytics and decision support by advanced AI and Smart technologies. It supports policymakers by providing intended and potential impact analysis for data-informed sound policy decisions. After implementing a carefully designed policy, the effectiveness of the policy for the targeted community needs to be measured. In this talk, I present a Trust-based Public Policy Platform (TBPPP) that considers as design requirements the risks and trustworthiness of the automated algorithmic machines with uber-intelligence, such as AI and predictive Machine Learning models. Secondly, the assessment of the policy effectiveness and impact must be continuous rather than relying only on traditional measurement instruments such as surveys. We present a method of continuous policy impact assessment using indirect indicators such as citizens, Äô perceptions of policies. The ability of continuous monitoring citizen, Äôs perceptions and sentiments as an alternative assessment tool allows policy implementers and the public to understand how policy adoption impacts different geographic regions and trends over time. A pilot system to monitor multiple COVID-19 health policies is introduced to gain insights into different policy impacts and to aid the public with understanding of the health-related government policy actions.