P21 AI-Driven Organisational Policymaking Across Disciplines

Panel Chairs

Corresponding Panel Chair

Homa Molavi Affiliation: The University of Manchester Country: United Kingdom
Email Address: molavihoma@yahoo.com

Co-Chairs

Dr Lihong Zhang, The University of Manchester, UK

Dr Kamal Qazi, The University of Manchester, UK

Dr Ian Stewart, The University of Manchester, UK

Prof. Mahdi Salehi, Ferdowsi University of Mashhad

Dr Radi Haloub, University College London (UCL), UK

Aims and scope

In an era of rapid technological advancement, artificial intelligence (AI) has emerged as a transformative force reshaping diverse sectors and disciplines, from public management to finance, law, education, project management, engineering, and beyond. Traditionally, policymaking has been characterized by lengthy, complex, and often bureaucratic processes that rely heavily on human judgment and historical data. However, AI is poised to revolutionize this landscape by introducing data-driven, analytical, and predictive capabilities that can streamline decision-making processes, reduce costs, and enhance the effectiveness and precision of policies. AI-driven approaches to policymaking encompass several advanced techniques, including Predictive Analytics, Data-Driven Decision Making, Agent-Based Modelling (ABM), Natural Language Processing (NLP), Decision Support Systems (DSS), Optimization Algorithms, and Cognitive Computing.

Discussion points:

• How can AI-driven approaches such as Predictive Analytics and Data-Driven Decision Making be effectively integrated into policymaking processes across different sectors (e.g., finance, law, education, engineering)?
• How do organisations manage the challenges of AI in their public administration?
• How will organisational innovations affect the design of AI-driven governance and policy?
• What are the differences between traditional governance and policy formulation and AI-driven governance and policy design?
• How can organisations effectively manage the advantages and risks associated with AI-driven policies?
• How can we connect AI-driven governance and policy frameworks with public administration theories?
• How can we create AI-powered tools and platforms to assist policymakers in data analysis, prediction, and decision-making?
• How can we implement pilot projects to test the effectiveness of AI in specific policy areas, such as healthcare, education, and transportation?
• How effective are AI-driven policies in achieving their intended outcomes compared to traditional policymaking methods?
• What are the measurable impacts of AI on policy implementation and public service delivery? What methods can be developed to integrate diverse data sources for more comprehensive policy analysis?
• How can interdisciplinary collaboration be fostered to improve the development and implementation of AI-driven policies?
• What roles do data scientists, accountants, policymakers, and domain experts play in creating effective AI-driven policies?
• How can we evaluate policy simulations and scenario analyses designed by AI?
• How can we create simulation models to predict the outcomes of various policy options using AI?
• How can we develop standards for data collection, storage, and sharing to facilitate AI-driven policymaking?
• How can we establish protocols for data privacy and security to protect sensitive information in AI-driven policymaking?
• What lessons learned and recommendations can be shared through real case studies to assist other organisations in implementing AI in their policymaking processes?

These questions are not exhaustive, and we invite submissions that cover other issues and topics that fall within the aims and scope of this panel.

Methods

We accept a range of research methods, including Case Studies, Simulation and Scenario Analysis, Surveys and Interviews, Literature Reviews, etc.