User:Brestaino/Public Policy

Additions to the Article
I will contribute to the Public Policy article by adding to the subsection on Data driven policy by adding more information concerning how governments use data for decision making and by adding examples.

Data Driven Policy
In the 2020s, policymakers will use data for policies and public service design, while responding to citizen engagement demands.The Anticipatory Governance model is particularly important when considering the sheer amount of data available. In terms of using new technology to collect, analyze, and disseminate data, governments are only just beginning to utilize data science for policy implementation. With new technologies implemented in government administration, a more complete visualization of current problems will emerge, allowing for more precision in targeted policy-making. Data science involves the transformation, analysis, visualization, and presentation of data, and potentially improve the quality of life and society by providing a more informational environment for public debate and political decision-making. Some examples of utilizing data science in public policy making are resource optimization, improving current public services, and fraud and error mitigation.

Data sets rarely merge between government agencies or within agencies or countries' governments. This is beginning to change with the COVID-19 pandemic spreading globally in early 2020. Forecasting and creating data models to prevent the propagation of the virus has become a vital approach for policy makers in governments around the world.

Artificial Intelligence and Public Policy
Artificial intelligence (AI) has been used in recent years by public administrators to deliver services and for the general improvement of government operations. In the realm of policy making in the public sector, AI will also be used to optimize outcome forecasting, pattern perception, and most importantly for the development of evidence-based programs to generate sound policy.

Using AI in government will continue to be used as an e-governance tool through virtual assistance on government websites and the automation of public online services. This will free public employees of answering frequently asked questions about government services or querying databases for information.

A drawback of using AI in public policy making and implementation is the concept of "algorithmic bias". Algorithmic bias can cause the government use of AI to have errors in decision making and create distrust in government entities.