AI4PublicPolicy Policy Evaluation Demonstration
In this video demonstration, Luca Marangoni from GFT Italy presents the Policy Evaluation component of the VPME that involves policymakers and other stackeholders in the policymaking process.
In this video demonstration, the Policy Evaluation component of the VPME that involves policymakers and other stackeholders in the policymaking process is presented. The Use Case considered for this demo, is the optimization of parking space allocation of the Athens pilot to achieve the highest level of satisfaction for the residents.
A virtual environment process has been designed to collect explicit and implicit feedback from local actors in order to evaluate and optimize policy models. The tools have been designed to include several types of surveys that can be used at different stages of a ML pipeline.
In the data collection phase, data sources are chosen and the policy is defined. At this stage, initial feedback from local actors can be asked, such as opinions on the usefulness of a policy or the input features of an AI algorithm. Feedback can also be requested later in the pipeline, once the AI algorithm has been trained and the policy model is available.
In the virtual simulation phase, surveys are created and the responses from the citizens are collected. The resulting insights can be used by the policy maker to evaluate and optimize the policy and policy models.
Finally, the policy maker can evaluate the created survey using the graphs and charts used to represent survey results.
For more information you may watch the demo below: